Diabetes-related biomarkers and treatment of diabetes-related conditions

ABSTRACT

The present invention provides biomarkers useful for evaluating the risk that a subject will develop diabetes, monitoring such risk, identifying members of a population at risk of developing diabetes, calculating risk of a subject developing diabetes, advising subjects of risk for developing diabetes, providing diagnostic tests for identifying subjects at risk for developing diabetes or kits there for, and providing diagnostic tests for determining risk of a subject developing diabetes and kits there for. The present invention also provides compounds and methods for treating subjects.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional application No.62/243,131 filed 18 Oct. 2015 which is hereby incorporated by reference.

TECHNICAL FIELD

The present invention relates to biomarkers associated with diabetes,methods of using the biomarkers to determine the risk that an individualwill develop diabetes, and methods of screening a population to identifypersons at risk for developing diabetes.

BACKGROUND

Diabetes is a serious illness characterized by a loss of the ability toregulate blood glucose levels. The World Health organization (WHO)estimates that more than 180 million people worldwide have diabetes.This number is likely to more than double by 2030. In 2005, an estimated1.1 million people died from diabetes, this estimate likely undercountsdeaths caused by diabetes, as diabetes contributes to other diseases,such as heart disease and kidney disease that may be listed as the causeof death. Almost 80% of diabetes deaths occur in low and middle-incomecountries. In China, the prevalence rate of diabetes is 9.7% in peopleover the age of 20, among which the rate is 10.6% for males and 8.8% forfemales. Based on this prevalence, the total number of diabetes isestimated to be 92.4 million, ranking the first in the world. Meanwhile,the prevalence rate of pre-diabetes is 15.5%, with an estimated numberof 148 million.

Obesity, the result of chronic nutrient imbalance, is closely associatedwith the increased risk of type 2 diabetes (T2D). Even within themetabolically healthy (MH), adjusted diabetes risk has been reported tobe 2-fold higher in overweight and 4-fold higher or 12-fold higher inobese compared with those normal weight (NW) individuals. Thus, theidentification of early metabolic abnormalities can be used to recognizethe higher risk overweight or obese (OW/OB) individuals and predictprogression to T2D.

What is needed in the art is a biomarker or combination of biomarkerscapable not only of identifying diabetes but also of determining therisk that an individual will develop diabetes.

SUMMARY OF THE INVENTION

The present invention relates to the identification of biomarkersassociated with subjects having diabetes, pre-diabetes, or apre-diabetic condition. The present invention provides biomarkers usefulfor evaluating the risk that a subject will develop diabetes, monitoringsuch risk, identifying members of a population at risk of developingdiabetes, calculating risk of a subject developing diabetes, advisingsubjects of risk for developing diabetes, providing diagnostic tests foridentifying subjects at risk for developing diabetes or kits there for,and providing diagnostic tests for determining risk of a subjectdeveloping diabetes and kits there for.

A first aspect of the invention provides a panel of biomarkers. A panelof biomarkers taught herein can be measured and used to evaluate therisk that a subject will develop diabetes (‘diabetes risk’) in thefuture, for example, the risk that an individual will develop diabetesin the next 1, 2, 5, or 10 years. The panel comprises a set of one ormore biomarkers that can be employed for methods, kits, computerreadable media, systems, and other aspects of the invention which employa panel of biomarkers. The panel can comprise one or more biomarkerslisted in FIG. 11. Optionally, the panel comprises a panel selected frompanels A-BZ listed in FIG. 12A-12C.

A second aspect of the invention provides a diagnostic method. Adiagnostic method of the invention comprises measuring, in a biologicalsample (‘sample’) obtained from a subject, the level of each biomarkerof a panel taught herein, and evaluating the diabetes status of thesubject based on the measured level.

Optionally, the diabetes status can include, for example, the risk ofdeveloping diabetes or a change in risk of developing diabetes. Forexample evaluating diabetes status can comprise, for example, evaluatingdiabetes risk or monitoring diabetes status (e.g. evaluating the changein diabetes risk).

Optionally, the step of evaluating can comprises correlating themeasured level of each biomarker (e.g. individually or collectively)with diabetes risk. For example, correlating can comprise:

-   -   a. comparing the measured level of each biomarker of the panel        to a comparator level (e.g. a level indicative of diabetes        risk), and evaluating diabetes risk based on the comparison; or    -   b. evaluating diabetes risk based on an output from a model        (e.g. algorithm), wherein the model is executed based on an        input of the measured level of each biomarker of the panel.

Optionally, the step of evaluating diabetes risk comprises determiningthat the subject is at risk for developing diabetes. Optionally, thestep of evaluating comprises calculating risk or likelihood of thesubject developing diabetes (e.g. calculating a risk score).

Optionally, the subject is any of: a metabolically healthy subject, asubject that that has not been previously diagnosed as at risk fordeveloping diabetes, a subject that that has not been previouslydiagnosed as having diabetes or pre-diabetes, and a subject that hasundergone treatment that reduces diabetes or diabetes risk (e.g.metabolic surgery or a very low carbohydrate diet).

Optionally, the sample is blood, plasma, or serum.

Optionally, the method of measuring comprises mass spectrometry (‘MS’).The MS can be performed following, for example, a separate techniquesuch as gas chromatography (‘GC’) or liquid chromatography (‘LC’).Optionally, the MS comprises GC-MS, LC-MS, or ultra-performance liquidchromatography-triple quadrupole mass spectrometry (UPLC-TQ).

A third aspect of the invention provides a method of treatmentcomprising administering or recommending (hereinafter individually andcollectively referred to as ‘administering’) treatment to a subject thathas been evaluated as at risk for developing diabetes by a diagnosticmethod according to the invention, a treatment that delays or preventsthe onset of diabetes.

Optionally, the treatment is any standard of care for a subject havingdiabetes.

Optionally, the treatment is a treatment that reduces the deviationbetween the level of one or more biomarkers of the panel exhibited bythe subject and a comparator level that is indicative of reduceddiabetes risk.

Optionally, the treatment comprises metabolic surgery, a very lowcarbohydrate diet (‘VLCD’), carbohydrate restriction, or calorierestriction.

Optionally, the treatment comprises an anti-diabetic agent.

Optionally, the method further comprises, following administering thetreatment, measuring, the level of each biomarker of the panel in asample obtained from the subject post-treatment, and comparing thepost-treatment level to the pre-treatment level, and evaluating whetherthe treatment has reduced the diabetes risk of the subject. Optionally,the method further comprises modifying the treatment following adetermination that the diabetes risk has not been reduced by thetreatment.

A fourth aspect of the invention provides a kit comprising one or morereagents for detecting a panel of one or more biomarkers taught herein.The one or more reagents comprise one or more internal standards,wherein, collectively, the one or more internal standards provide atleast one internal standard for each biomarker of the panel. Optionally,the kit comprises a mixture of said internal standards (e.g. a mixturethereof). Optionally, the one or more or more internal standards arelyophilized. Optionally, one or more reagents are provided in acontainer.

In any aspect of the invention, the panel optionally comprises one ormore bile acids, one or more amino acids, one or more free fatty acids,and/or one or more one or more blood biochemical index biomarkers.

Optionally, the one or more bile acids are selected fromGlycohyodeoxycholic acid (‘GHDCA’), Taurohyodeoxycholic acid (‘THDCA’),Hyodeoxycholic acid (‘HDCA’), Taurochenodeoxycholic acid (‘TCDCA’),Taurodeoxycholic acid (‘TDCA’), Glycohyocholic acid (‘GHCA’), Hyocholicacid (‘HCA’), Taurohyocholic acid (‘THCA’), and Taurolithocholic acid(‘TLCA’).

Optionally, the one or more one or more amino acids comprise one or morebranch chain amino acids (‘BCAAs’) and/or one or more aromatic aminoacids (‘AAAs’). Optionally, the BCAAs comprises one or more of leucine,isoleucine, and valine. Optionally, the AAAs comprises phenylalanineand/or tyrosine.

Optionally, the one or more one or more amino acids comprise one or moreof Alanine, Aspartic acid, Beta-Alanine, Creatine, Cystine, Glycine,Histidine, Isoleucine, Leucine, Methionine, N-Acetyl-L-aspartic acid,Proline, Pyroglutamic acid, Serine, S-Methyl-cysteine, Threonine,Tryptophan, Tyrosine, Valine, and Phenylalnine.

Optionally, the one or more one or more free fatty acids comprises oneor more of Lauric acid (C12:0), Myristic acid (C14:0),12-Methyltridecanoic acid (C14:0 iso), Myristoleic acid (C14:1 n5),13-Methylmyristic acid (C15:0 iso), Pentadecanoic acid (C15:0), Palmiticacid (C16:0), 14-methylpentadecanoic acid (C16:0 iso), Palmitoleic acid(C16:1 n7), Palmitelaidic acid (C16:1 t9), 15-Methylpalmitic acid (C17:0iso), Margaric acid (C17:0), Stearic acid (C18:0), 16-Methylmargaricacid (C18:0 iso), Oleic acid (C18:1 n9), Elaidic acid (C18:1 t9),Linoleic acid (C18:2 n6), α-Linolenic acid (C18:3 n3), Nonadecanoic acid(C19:0), Nonadecenoic acid (C19:1 n9), Eicosenoic acid (C20:1 n9),dihomo-γ-linolenic acid (C20:3 n6), Arachidonic acid (C20:4 n6), Erucicacid (C22:1 n9), Docosatetraenoic acid (C22:4 n-6), Docosapentaenoicacid (C22:5 n-6), and Eicosenoic acid (C20:1 n-9).

Optionally, the one or more blood biochemical index biomarkers compriseone or more of total Triglycerides (TG), Gemoglobin A1c (HBA1c),Glucose, Insulin, High density lipoprotein (HDL), and Low densitylipoprotein (LDL).

Optionally, the panel comprises:

-   -   a. one or more (e.g. each) of GHDCA or % GHDCA of total bile        acids, THDCA or % THDCA, HDCA or % HDCA of total bile acids, HCA        or % HCA of total bile acids, GHCA or % GHCA, and THCA or %        THCA;    -   b. one or more (e.g. each) of Palmitic acid (C16:0), Stearic        acid (C18:0), Oleic acid (C18:1 n9), dihomo-γ-linolenic acid        (C20:3 n6), and Arachidonic acid (C20:4 n6);    -   c. one or more (e.g. each) of Isoleucine, Leucine, Tyrosine,        Valine, and Phenylalanine;    -   d. TG;    -   e. a combination of (a) and (b), (a) and (c), (a) and (d), (b)        and (c), (b) and (d), or (c) and (d);    -   f. a combination (a), (b), and (c);    -   g. a combination (a), (b), and (d);    -   h. a combination (a), (c), and (d); or    -   i. a combination (b), (c), and (d).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an ROC curve useful in carrying out a method of theinvention.

FIG. 2 depicts an ROC curve useful in carrying out a method of theinvention.

FIG. 3 depicts an ROC curve useful in carrying out a method of theinvention.

FIG. 4 depicts an ROC curve useful in carrying out a method of theinvention.

FIG. 5 depicts an ROC curve useful in carrying out a method of theinvention

FIG. 6 depicts an ROC curve useful in carrying out a method of theinvention. The data was produced from 10 years follow-up serum samplesusing P/S (C16:0/C18:0) ratio.

FIG. 7 depicts an ROC curve useful in carrying out a method of theinvention. The data was produced from 10 years follow-up serum samplesusing the combination of P/S (C16:0/C18:0) ratio and TG;

FIG. 8 depicts an ROC curve useful in carrying out a method of theinvention. The data was produced from 10 years follow-up serum samplesusing the combination of P/S (C16:0/C18:0) ratio, O/S ratio (C18:1n9/C18:0) and DGLA/A ratio (C20:3 n6/C20:4 n6)

FIG. 9A and FIG. 9B depict a kit of the invention.

FIG. 10 depicts a kit of the invention.

FIG. 11 depicts biomarkers useful in the present invention.

FIGS. 12A-12C depict biomarker panels useful in the present invention.The biomarker panels are identified as panels A through BZ.

FIG. 13 depicts the levels of TGR5 and GLP1 after treatment withdifferent bile acids and controls (WT, wildtype; and solvent, DMSO).

DETAILED DESCRIPTION OF THE INVENTION

Methods

Overview

One embodiment of the invention provides a diagnostic method comprisingevaluating diabetes status (e.g. diabetes risk) in a subject. Diabetesrisk can optionally be evaluated by measuring, in a sample obtained fromthe subject, the level of one or more biomarkers of a panel taughtherein and correlating the measured level of the level of one or morebiomarkers with diabetes risk. Based on the correlation, the inventioncan evaluate subjects as having an increased risk of developingdiabetes, thereby identifying subjects having an increased risk ofdeveloping diabetes.

Optionally, subjects identified as having an increased risk ofdeveloping diabetes and are selected to receive treatment to delay orprevent the onset of diabetes, or reduce diabetes risk.

Optionally, the method comprises monitoring the level of the one or morebiomarker of the panel. Methods of monitoring can comprise providing afirst biological sample can be provided from the subject at a first time(e.g. prior to undergoing treatment) and a second biological sample canbe provided from the subject at a later time (e.g. following thetreatment), and measuring the level of the one or more biomarkers in thefirst sample and second sample.

Subject

Methods of the invention can measure the level of one or more biomarkersin a biological sample obtained from a subject. The subject can be anysubject, e.g. a human subject.

The invention is surprisingly useful for correlating diabetes risk in asubject when implemented with subjects that do or do not exhibit typicalphenotypes of diabetes or diabetes risk.

Optionally, the subject is a metabolically healthy subject.

Optionally, the subject is any of: a metabolically healthy subject, asubject that that has not been previously diagnosed as at risk fordeveloping diabetes, a subject that that has not been previouslydiagnosed as having diabetes or pre-diabetes, and a subject that hasundergone treatment that reduces diabetes or diabetes risk (e.g.metabolic surgery or a very low carbohydrate diet).

Optionally, the subject is a metabolically healthy. For example, thesubject optionally exhibits one or more or each of the following: FPG≤6.1 mmol/L, OGTT ≤7.8 mmol/L, SBP/DBP <140/90 mmHg, fasting plasma TG<1.7 mmol, fasting plasma HDL≥0.9 mmol/L (for men) or ≥1.0 mmol/L (forwomen). Optionally, the subject has no previous history of highcholesterol (TC <5.18 mmol/L); no cardiovascular or endocrine diseasehistory, has not been previously diagnosed with diabetes, and/or has noprevious history of high blood pressure.

Optionally, the subject exhibits one or more (e.g. each) of low fastingblood glucose, insulin, or HBA1c levels. For example, the subject canexhibit ≤10 mU/mL fasting insulin level, less than 6% fasting HBA1clevel, and/or ≤126 mg/dl fasting glucose level.

Optionally, the subject does not exhibit hypertension, e.g. consistent(e.g. repeated measurements over time of) blood pressure above 140/90.

Optionally, the subject does not exhibit an LDL cholesterol level above137 and/or total cholesterol level above 200.

Optionally, the subject is a non-obese subject. For example, the subjectcan have a BMI of less than 30, less than 29, or less than 25.

Optionally, the subject is not overweight. For example, the subject canhave a BMI of less than 24.

Optionally, the subject is overweight. For example, the subject can havea BMI of at least 24.

Optionally, the subject a non-morbidly obese subject. For example, thesubject can have a BMI of less than 35.

Optionally, the subject is a woman having a fat percentage of ≤30% forwomen or a man having a body fat percentage of ≤25%.

Optionally, the subject has a BMI of at least 24 or at least 28 and thesubject exhibits FPG ≤6.1 mmol/L, OGTT ≤7.8 mmol/L, SBP/DBP <140/90mmHg, fasting plasma TG <1.7 mmol, fasting plasma HDL≥0.9 mmol/L (formen) or ≥1.0 mmol/L (for women).

Optionally, the subject has a BMI of less than 28 or less than 24 andthe subject exhibits FPG ≤6.1 mmol/L, OGTT ≤7.8 mmol/L, SBP/DBP <140/90mmHg, fasting plasma TG <1.7 mmol, fasting plasma HDL≥0.9 mmol/L (formen) or ≥1.0 mmol/L (for women).

Sample

Methods of the invention can measure the level of one or more biomarkersin a biological sample (‘sample’) obtained from a subject.

Optionally, the sample comprises blood, plasma, or serum. For example,any of such samples can be obtained from a subject following a period offasting. The collection of fasting samples is well-known in the art.

Optionally, the sample comprises or consists of serum. As used herein,“serum” refers to the fluid portion of the blood obtained after theremoval of the fibrin clot and blood cells, distinguished from theplasma in circulating blood.

Internal Standards

Methods of the invention optionally comprise the use of an internalstandard for one or more (e.g. each) of the biomarkers that aremeasured. An internal standard can optionally be mixed with the sampleat any time prior to measurement.

Optionally, the internal standard, having a known initial level in thesample prior to sample preparation, can provide a measurement signalused to normalize the signal of the respective biomarker.

Steps of sample preparation can sometimes induce substantial loss ofbiomarker prior to measurement. While the development of a predictablesample preparation technique and use of the same separation andmeasurement instruments (e.g. the same LCMS machine) can increaseaccuracy and precision of measurement, the use of different samplepreparation mediums, methods, and measurement machines can induceunpredictable changes in biomarker recovery and/or measurement signal.Accordingly, an internal standard can optionally be used in the presentinvention to correct for loss (i.e. recovery inconsistencies) and/orsignal level variation of a respective biomarker during samplepreparation and measurement. For example, an internal standard can beadded to the sample after sample collection but before samplepreparation for measurement (e.g. before filtering, extraction, and/orprecipitation steps).

Optionally, the internal standards are configured for GC-MS or LC-MS.

Optionally, an internal standard is provided that is the same compoundas a corresponding biomarker of the panel, except it has one or more ofits atoms replaced with a stable isotope of the one or more atoms (e.g.(2)H, (13)C, (15)N, or (18)O). For example, a set of internal standardsfor a given panel of biomarkers can be provided by providing an isotopelabeled variant of each biomarker of the panel.

Optionally, an internal standard compound is chemically similar, but notidentical to the respective one or more biomarkers that are measuredsuch that the internal standard exhibits similar behavior during samplepreparation but can be uniquely identified during the measurement step.Optionally, the internal standard is selected such that effects ofsample preparation on the measurement signal level of internal standardshould be the same relative to the measurement signal level of therespective biomarker.

Optionally, the internal standard is mixed with the sample prior to oneor more steps of preparation (e.g. extraction or other purification),and biomarker separation steps.

Optionally, the panel comprises a plurality of biomarkers and adifferent internal standard for each biomarker (e.g. a labeled biomarkeridentical to the respective biomarker other than the label).Alternatively, at least one internal standard is provided fornormalization of a plurality of biomarkers (e.g. a biomarker of the samecompound class one or more respective biomarkers such as a labeledsteroid acid or bile acid for a plurality of bile acids, a labeled fattyacid for a plurality of fatty acid biomarkers, and/or a labeled aminoacid for a plurality of amino acids). For example, nonadecanoic-d37 acidcan optionally be provided as an internal standard for all of the freefatty acids of a panel taught herein.

Optionally, the internal standard is an isotope labeled compound.

Alternatively, the internal standard is any compound not found in thesample, e.g. not found in blood, plasma, or serum.

Optionally, the internal standard is a stable isotope labeled compound(e.g. labeled variant of the biomarker or labeled variant of a compoundwith similar properties as the biomarker). Optionally, the stableisotope is (2)H, (13)C, (15)N, or (18)O.

Optionally, one or more internal standards are selected from a labeledsteroid acid such as a bile acid (e.g. used for a bile acid biomarker),a labeled fatty acid (e.g. used for a fatty acid biomarker) and/or alabeled amino acid (e.g. used for an amino acid biomarker).

Optionally, useful internal standards are any stable-isotope labeledvariants of respective biomarkers to be determined in the samples.Optionally, the internal standards have the same chemical properties asthe metabolite biomarkers to be measured from the panel. For example,with respect to a corresponding biomarker of the panel, the internalstandard can be selected such that it produces the same recovery percentwhen extracted by protein precipitation (e.g. by Ammonium sulfate,trichloroacetic acid (TCA), acetone, or ethanol) and/or filtration (e.g.using filters taught herein) from the sample. Given a particularbiomarker panel, the skilled artisan can readily select internalstandards that have the same or shared chemical properties as acorresponding biomarker, wherein the same or shared chemical propertiesare those that influence the recovery percentage of a biomarker andinternal standard (e.g. molecular weight, polarity, shared R-groups,etc.). Optionally, the recovery rate of each internal standard issubstantially the same as the respective biomarker, for example, lessthan 10%, 7%, 5%, 4%, 3%, 2%, 1%, or 0.5% difference in recovery raterelative to the corresponding biomarker.

For example, the following illustrate useful internal standards ofcorresponding biomarkers measurement in the present invention:

cholic acid (CA)-d4 (e.g. used for hyocholic acid (HCA’);

ursodeoxycholic acid (UDCA)-d4 (e.g. used for glycohyodeoxycholic acid(GHDCA), taurochenodeoxycholic acid (TCDCA), taurodeoxycholic acid(‘TDCA’), glycohyocholic acid (‘GHCA’), and/or taurohyocholic acid(‘THCA’));

lithocholic acid (LCA)-d4 (e.g. used for taurolithocholic acid(‘TLCA’));

tridecanoic-d25 acid (e.g. used for monounsaturated free fatty acids);

nonadecanoic-d37 acid (e.g. used for saturated free fatty acids);

Valine-d8 (used for valine);

Leucine-5,5,5-d3 (used for leucine);

Isoleucine-2-d1 (used for isoleucine);

Tyrosine-3,3-d2 (used for tyrosine); and

Phenylalanine-3,3-d2 (used for phenylalanine)

With the teachings provided herein, the skilled artisan can readilyselect useful internal standards based on the selection of a biomarkerpanel.

Sample Preparation

The biomarkers measured in the present invention can optionally beextracted from a sample obtained from a subject. For example, the methodoptionally comprises steps of contacting the sample with an extractionmedium, extracting the biomarkers from the sample, and measuring theextracted lipids (e.g. by mass spectrometry following separation of thebiomarkers by chromatography).

The extraction medium can be any medium that allows the extraction ofbiomarkers from one or more other components of the sample that are notmeasured in the measuring step. Optionally, the extraction mediumcomprises a solution (e.g. protein precipitating solution), a filter, orboth.

Measurement

Methods of the invention optionally measure the level of one or morebiomarkers that are present in a biological sample. The step ofmeasuring can be performed in any manner useful to measure thebiomarkers taught herein.

Optionally, the method of measuring comprises mass spectrometry (‘MS’).

The MS can be performed following, for example, a separate techniquesuch as gas chromatography (‘GC’) or liquid chromatography (‘LC’).Optionally, the MS comprises GC-MS, LC-MS, or ultra-performance liquidchromatography-triple quadrupole mass spectrometry (UPLC-TQ).

Optionally, the step of measurement is preceded by steps of extraction(e.g. filtration) and/or separation (e.g. chromatography).Chromatography is a method of separating components in a sample based ondifferences in partitioning behavior between a mobile phase and astationary phase. Typically, a column holds the stationary phase and themobile phase carries the sample through the column. Sample componentsthat partition strongly into the stationary phase spend a greater amountof time in the column and are separated from components that staypredominantly in the mobile phase and pass through the column faster. Asthe components elute from the column, they can be measured, e.g. using amass spectrometer.

Optionally, each of the biomarkers of a panel taught herein is measuredfrom the same aliquot of the sample following instruction of the aliquotin a chromatography column. Such an embodiment can separate all of thebiomarkers from a single aliquot and provides an efficient method byeliminating the need for parallel sample preparation, extraction, andseparation steps.

Such a simultaneous running of all biomarkers can be provided bytailoring the chromatographic conditions, e.g. the column temperature,the composition of mobile phase, flow rate, eluent condition of mobilephase, analytical time as well as the mass fragmentation pattern (parention and daughter ion of the biomarker to be measured) of metabolites toget adequate separation.

When the biomarker of interest is an analyte (e.g. palmitic acid orstearic acid), the step of “measuring” can include measuring the level(e.g. measuring a signal indicative of the level) of the analyte. Whenbiomarker of interest is a calculation based on of a plurality ofanalytes such as ratio (e.g. P/S ratio), percentage (e.g. % GHDCA oftotal bile acids), or sum (e.g. TG, total triglycerides), the step ofmeasuring can include measuring the levels of each of the plurality ofanalytes and calculating the level of the biomarker based on themeasured levels.

The term “level”, as used herein with respect to a biomarker, can be anyquantitative or qualitative representation of the presence of thebiomarker in the sample, e.g. the amount (e.g. mass) or concentration(e.g. w/w, w/v, or molarity).

When expressed as a concentration, the level can be expressed withrespect to the volume (or weight) of the obtained sample prior to samplepreparation (e.g. extraction).

Correlation

According to a method of the invention, evaluating diabetes status canoptionally comprise correlating the measured level of one or morebiomarkers of the panel with diabetes risk. Based on the correlation,the risk of developing diabetes (e.g. likelihood of developing diabetes)can optionally be determined. For example, it can be determined whetherthe subject is likely (e.g. whether likelihood is greater than a presetlikelihood threshold) to develop diabetes or has a high risk (e.g.greater than a risk threshold) for developing diabetes. Such subjectsare sometimes referred to herein as being at risk for developingdiabetes. Subjects determined to be at risk can optionally be identifiedand/or selected, e.g. to receive advice and/or treatment.

Optionally, the step of correlating comprises comparing one or moremeasured biomarker levels to respective comparator levels (e.g. a levelindicative of low risk or no risk, or a threshold level thatdistinguishes risk levels). Additionally or alternatively, measuredlevels of one or a plurality of biomarkers can be inputted into a model(e.g. mathematical formula or algorithm) that computes a single scorebased on the measured levels. Such as score can itself be correlatedwith risk of developing diabetes (e.g. where the score is compared to athreshold score indicative of a clinical endpoint such as thedevelopment of diabetes within a time period).

A biomarker level (e.g. measured level) or a score (e.g. computed bymodel) can be compared to a comparator level or comparator score,respectfully, utilizing techniques such as reference limits,discrimination limits, or risk defining thresholds to define cutoffpoints and abnormal values for a state such as diabetes risk. Thecomparator level of a biomarker or combined biomarker score can be, forexample, a level or score, respectively, typically found in a subjectnot at risk for developing diabetes or at low risk for developingdiabetes (or conversely a subject at risk or at high risk). The risk cango up or down, depending on whether a subject's measured level or scoreis greater or lower than the comparator level or score (e.g. notingthat, with respect to Table 3, all biomarkers, other than GHDCA and %GHDCA, were increased in subjects that later developed diabetes andGHDCA and % GHDCA were decreased in subjects that later developeddiabetes). Such comparator levels and cutoff points may optionally varybased on whether a biomarker is used alone or in a formula combiningwith other biomarkers into a score. Alternatively, a comparator level orscore can be determined from previously tested subjects who did notdevelop diabetes over a clinically relevant time horizon (e.g. 1, 2, 5,or 10 years), or a threshold that was determined to distinguish subjectswho developed diabetes from subjects who did not did not developdiabetes over a clinically relevant time horizon (e.g. as taught in theExamples). Optionally, the comparator level or comparator score is thelevel or score, respectively, that distinguishes between high risk andlow risk subjects. A high risk score or likely diabetes development canbe defined at-will by the clinician, and can indicate for example, atleast 70% risk (e.g. wherein 70% of the subjects studied that had alevel or score at or above the comparator developed diabetes over thetime horizon), at least 75% risk, at least 80% risk, at least 85% risk,at least 90% risk, at least 85% risk, at least 90% risk, or at least95%. Additionally or alternatively, several risk classifications (e.g.very low, low, medium, high, and very high) can be provided, whereineach classification is identified by a given level or score range.Additionally or alternatively, an equation (e.g. curve) can be providedthat correlates level or score to risk (e.g. relative or absolute) orlikelihood (e.g. percent likelihood) of developing diabetes.

Optionally, a profile is provided comprising each of the measured orcalculated levels of the one or more biomarkers of a panel and themeasured profile is compared to a comparator biomarker profile. Thebiomarkers of the present invention can be used to generate a comparatorbiomarker profile. A comparator biomarker profile can be a low risk orno risk comparator, e.g. taken from subjects who did not developdiabetes or who rarely developed diabetes within a clinicallysignificant time period following obtainment of the sample thatexhibited the biomarker profile. Alternatively, a comparator profile canbe a high risk comparator, e.g. taken from subjects that developeddiabetes or who usually developed diabetes following the clinicallysignificant time period following obtainment of the sample thatexhibited the biomarkers. Optionally, the high risk comparator profilecan be compared to the low risk profile to identify subjects at risk fordeveloping diabetes, to monitor the progression of disease, to monitorthe progression of diabetes risk, or to monitor the effectiveness oftreatment. Optionally, the high risk comparator profile and/or low riskcomparator profile can be stored on a non-volatile memory.

Optionally, a method of the invention uses a model (e.g. algorithm) tocorrelate measured biomarkers levels with diabetes risk. Suchcorrelation using a model can be performed on a system configured therefor, e.g. a computer having a program that implements the model andcalculates a score based on the input of measured marker levels.Optionally, measurements of a biomarker panel of the present inventionserve as inputs to a computer or microprocessor programmed with a modelthat implements an algorithm that computes a risk score. If some factors(e.g. physiological factors such as BMI, age, and sex) in addition tothe biomarkers tested in the system are used to calculate the final riskscore, then these factors can be supplied to the model so that it cancomplete the risk score calculation, or the algorithm can produce apreliminary score that will reported and externally combined with theother factors to calculate a final risk score.

The diabetes for which risk of development is evaluated can optionallybe any diabetic condition and can be identified by any endpointexhibited by subjects having a diabetic condition. The diabetes endpointcan include, for example, a two-hour glucose levels of at least 140(e.g. 140- to 199 mg/dL, sometimes used as an endpoint for pre-diabetes)or at least 200 mg/dl (sometimes used as an endpoint for classicaldiabetes). Additionally or alternatively, the diabetes endpoint caninclude a glycated hemoglobin (HbA1c) level of at least 5.7% (e.g. 5.7%and 6.4%, sometimes used as an endpoint for pre-diabetes) or of at least6.5% (sometimes used as an endpoint for classical diabetes). Thediabetes can be, for example, non-insulin dependent diabetes, and can bepre-diabetes or classical diabetes. Alternatively, any other endpointcan be used to classify diabetes, e.g. Metabolic Syndrome (“SyndromeX”), Impaired Glucose Tolerance (IGT), and Impaired Fasting Glycemia(IFG). IGT refers to post-prandial abnormalities of glucose regulation,while IFG refers to abnormalities that are measured in a fasting state.The World Health Organization defines values for IFG as a fasting plasmaglucose concentration of 6.1 mmol/L (100 mg/dL) or greater (whole blood5.6 mmol/L; 100 mg/dL), but less than 7.0 mmol/L (126 mg/dL) (wholeblood 6.1 mmol/L; 110 mg/dL). Metabolic syndrome according to theNational Cholesterol Education Program (NCEP) criteria is defined ashaving at least three of the following: blood pressure greater than orequal to 130/85 mm Hg; fasting plasma glucose greater than or equal to6.1 mmol/L; waist circumference >102 cm (men) or >88 cm (women);triglycerides greater than or equal to 1.7 mmol/L; and HDL cholesterol<1.0 mmol/L (men) or 1.3 mmol/L (women). Many individuals withpre-diabetic conditions will not convert to type 2 diabetes (‘T2DM’).“Impaired glucose tolerance” (IGT) is a pre-diabetic condition definedas having a blood glucose level that is higher than normal, but not highenough to be classified as Diabetes Mellitus. A subject with IGT willhave two-hour glucose levels of 140 to 199 mg/dL (7.8 to 11.0 mmol) onthe 75-g oral glucose tolerance test. These glucose levels are abovenormal but below the level that is diagnostic for Diabetes. Subjectswith impaired glucose tolerance or impaired fasting glucose have asignificant risk of developing Diabetes and thus are an important targetgroup for primary prevention. While these endpoints are well-knownendpoints of clinical diagnosis, the skilled artisan will appreciatethat such endpoints can be varied and produce a diabetes endpoint usefulaccording to the invention. Further, other endpoints that areinformative of diabetes or insulin resistance can also be used. Suchendpoints can optionally be used to train a comparator profile scorebased on historical subjects that did or did not develop diabetes, andthis comparator profile score can be used to compare against a subjectthat wishes to have his diabetes risk evaluated.

“Risk” in the context of the present invention, includes the probabilitythat an event will occur over a specific time period, e.g., as in theconversion to frank type 2 diabetes, and can mean a subject's “absolute”risk or “relative” risk. Absolute risk can be measured with reference toeither actual observation post-measurement for the relevant time cohort,or with reference to index values developed from statistically validhistorical cohorts that have been followed for the relevant time period.Relative risk refers to the ratio of absolute risks of a subjectcompared either to the absolute risks of low risk cohorts or an averagepopulation risk, which can vary by how clinical risk factors areassessed. Odds ratios, the proportion of positive events to negativeevents for a given test result, are also commonly used (odds areaccording to the formula p/(1−p) where p is the probability of event and(1−p) is the probability of no event) to no-conversion. Alternativecontinuous measures which may be assessed in the context of the presentinvention include time to diabetes conversion and therapeutic diabetesconversion risk reduction ratios.

“Risk evaluation”, in the context of the present invention encompassesmaking a prediction of the probability, odds, or likelihood that anevent or disease state may occur, the rate of occurrence of the event orconversion from one disease state to another, i.e., from a normoglycemiccondition to a pre-diabetic condition or pre-diabetes, or from apre-diabetic condition to pre-diabetes or diabetes. Risk evaluation canalso comprise prediction of future glucose, HBA1c scores or otherindices of diabetes, either in absolute or relative terms in referenceto a previously measured population. The methods of the presentinvention can optionally be used to make continuous or categoricalmeasurements of the risk of conversion to Type 2 Diabetes, thusdiagnosing and defining the risk spectrum of a category of subjectsdefined as pre-diabetic. In the categorical scenario, the invention canbe used to discriminate between normal and pre-diabetes subject cohorts.In other embodiments, the present invention may be used so as todiscriminate pre-diabetes from diabetes, or diabetes from normal. Suchdiffering use may optionally require different biomarker combinations inindividual panels, mathematical algorithm, and/or cut-off points, but besubject to the same aforementioned measurements of accuracy for theintended use.

In one embodiment, evaluation of diabetes risk comprises calculating arisk score. While certain scoring methods of the invention areexemplified using Logistic Regression-based correlation (e.g. asdetailed in the Examples), the invention contemplates any method ofcorrelation. For example, after selection of a set of biomarkers asdisclosed in the instant invention, well-known techniques such ascross-correlation, Principal Components Analysis (PCA), factor rotation,Logistic Regression (LogReg), Linear Discriminant Analysis (LDA),Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines(SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), relateddecision tree classification techniques, Shrunken Centroids (SC),StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, NeuralNetworks, Bayesian Networks, Support Vector Machines, and Hidden MarkovModels, Linear Regression or classification algorithms, NonlinearRegression or classification algorithms, analysis of variants (ANOVA),hierarchical analysis or clustering algorithms; hierarchical algorithmsusing decision trees; kernel based machine algorithms such as kernelpartial least squares algorithms, kernel matching pursuit algorithms,kernel Fisher's discriminate analysis algorithms, or kernel principalcomponents analysis algorithms, or other mathematical and statisticalmethods can be used to develop an algorithm for calculation of Diabetesrisk score. Generally, a selected population of individuals is used,where historical information is available regarding the values ofbiomarkers in the population and their clinical outcomes such as thedevelopment of diabetes (e.g. as detailed in the Examples). To calculatea diabetes risk score for a given individual, biomarker values canoptionally be obtained from one or more samples collected from theindividual and used as input data, i.e. input into a model fitted (e.g.algorithm) to the actual historical data obtained from the selectedpopulation of individuals.

The performance and thus absolute and relative clinical usefulness ofthe invention may be assessed in multiple ways as noted above. Amongstthe various assessments of performance, the invention is intended toprovide accuracy in prediction of diabetes risk and treatmentmonitoring. Accuracy concerns the ability of the test, assay, or methodto distinguish between subjects that will or will not develop diabetes,and is based on whether the subjects have an effective level or asubstantial alteration in the level of one or more biomarkers, or ascore calculated there from. By effective level or substantialalteration it is meant that the measurement of the biomarker isdifferent than the predetermined cut-off point (or threshold value) forthat biomarker or change in the level there of, respectively, andtherefore indicates that the subject is at risk for developing diabetesor has undergone a change in diabetes risk (e.g. in methods ofmonitoring). The difference in the level of biomarker between at risk(or high risk) and not at risk (or low risk) is preferably statisticallysignificant and may be an increase in biomarker level or a decrease inbiomarker level, as is readily apparent from the Examples taught herein.While the invention contemplates the use of a one-biomarker panel, forsome populations (e.g. exhibiting a specific genetic, physiological, orclinical state), achieving statistical significance, and thus thepreferred analytical and clinical accuracy, many include combinations ofseveral biomarkers to be used together in a panels and combined withmathematical models (e.g. algorithm) in order to achieve a statisticallysignificant risk score.

As with the categorical diagnosis of a disease state, changing the cutpoint or threshold value of a test for diabetes risk according to thepresent invention may change the sensitivity and specificity, but oftenin a qualitatively inverse relationship. Therefore, in assessing theaccuracy and usefulness of a proposed method, a test designer mayoptionally take both sensitivity and specificity into account and bemindful of what the cut point is at which the sensitivity andspecificity are being reported because sensitivity and specificity mayvary significantly over the range of cut points. Use of statistics suchas AUC, encompassing all potential cut point values, is sometimespreferred for risk evaluation using the invention.

Using such statistics, an acceptable degree of diagnostic accuracy usinga method of the invention to evaluate diabetes risk in which the AUC(area under the ROC curve for the test or assay) is optionally at least0.60, or greater such as at least 0.65, at least 0.70, at least 0.75, atleast 0.80, or at least 0.85. Optionally, the AUC is even greater, e.g.0.875, at least 0.90, or at least 0.95.

Optionally, by defining the degree of diagnostic accuracy, i.e., cutpoints on a ROC curve, defining an acceptable AUC value, and determiningthe acceptable ranges in relative concentration of what constitutes alevel (or score) of the biomarkers (individually or collectively) thatdistinguishes at-risk subjects from low risk subjects, the inventionallows one of skill in the art to use the biomarkers to identifysubjects that are at risk for developing diabetes with a high level ofpredictability and performance.

Models can also be developed and/or used as detailed below.

Development of a Scoring Model

While the Examples taught herein provide details of the development of amodel for carrying out the invention, the skilled artisan willappreciate that, using the biomarkers identified herein and therelationships taught herein, a mathematical model used to calculate ascore can be produced in any manner, and need not rely on the datacollected and presented herein.

For example, the model can be produced by obtaining biomarker level datafrom a representative population including data from those who did anddid not develop diabetes over a clinically significant time followingbiomarker level measurement (e.g. as detailed in the Examples). Suchdata can be obtained from the teachings provided herein (e.g. biomarkerdata detailed in the Examples), first-hand from the population,prospective (longitudinal) studies to involving observations of therepresentative population over a period of time, retrospective studiesof samples of a representative population that queries the samplesand/or from a retrospective epidemiological data storage containing theresults from previous studies, such as an NIH database. The biomarkerdata may be derived from a single study or multiple studies, andgenerally include data pertaining to the desired indication and endpointof the representative population, including values of the biomarkersdescribed herein, clinical annotations (which may include endpoints),and most particularly the desired endpoints for training an algorithmfor use in the invention, across many subjects. The biomarker level datais then optionally stored on non-volatile memory.

The representative population data set can then be prepared as needed tomeet the requirements of the model or analysis that will be used forbiomarker selection, as described below. For example, data setpreparation may include preparing the biomarker level values from eachsubject within the representative population, or a chosen subsetthereof. However, the raw biomarker level data alone may not be entirelyuseful for the purposes of model training. As such, various datapreparation methods may be used to prepare the data, such as gap filltechniques (e.g., nearest neighbor interpolation or other patternrecognition), quality checks, data combination using of various formulae(e.g., statistical classification algorithms), normalization and/ortransformations, such as logarithmic functions to change thedistribution of data to meet model requirements (e.g., base 10, naturallog, etc.). Again, the particular data preparation procedures aredependent upon the model or models that will be trained using therepresentative population data. The particular data preparationtechniques for various different model types are known, and need not bedescribed further.

The particular biomarkers are optionally selected to be subsequentlyused in the training of the model used to evaluate a risk of developinga diabetic condition. Biomarker selection may involve utilizing aselection model to validate the representative population data set andselecting the biomarker data from the data set that provides the mostreproducible results. Examples of data set validation may include, butare not limited to, cross-validation and bootstrapping. From thebiomarker selection, the model to be used in evaluating a risk ofdeveloping a diabetic condition may be determined and selected. However,it is noted that not all models provide the same results with the samedata set. For example, different models may utilize different numbers ofbiomarkers and produce different results, thereby adding significance tothe combination of biomarkers on the selected model. Accordingly,multiple selection models may be chosen and utilized with therepresentative population data set, or subsets of the data set, in orderto identify the optimal model for risk evaluation. Examples of theparticular models, including statistical models, algorithms, etc., whichmay be used for selecting the biomarkers have been described above.

For each selection model used with the data set, or subset thereof, thebiomarkers are optionally selected based on each biomarker's statisticalsignificance in the model. When input into each model, the biomarkersare optionally selected based on various criteria for statisticalsignificance, and may further involve cumulative voting and weighting.Tests for statistical significance may include exit-tests and analysisof variance (ANOVA). The model may include classification models (e.g.,LDA, logistic regression, SVM, RF, tree models, etc.) and survivalmodels (e.g., cox), many examples of which have been described above.

It is noted that while biomarkers may be applied individually to eachselection model to identify the statistically significant biomarkers, insome instances individual biomarkers alone may provide less predictivepower than desired, in which case combinations of biomarkers may beapplied to the selection model. For example, rather than utilizingunivariate biomarker selection, multivariate biomarker selection may beutilized. That is, a biomarker may not be a as good of an indicator whenused as a univariate input to the selection model, relative to when itis used in combination with other biomarkers (e.g., a multivariate inputto the model), because each marker may bring additional information tothe combination that would not be indicative if taken alone.

The model to be used for evaluating risk is selected, trained andvalidated. In particular, leading candidate models may be selected basedon one or more performance criteria, examples of which have beendescribed above. For example, from using the data set, or data subsets,with various models, not only are the models used to determinestatistically significant biomarkers, but the results may be used toselect the optimal models along with the biomarkers. As such, theevaluation model used to evaluate risk may include one of those used asa selection model, including classification models and survival models.Combinations of models markers, including marker subsets, may becompared and validated in subsets and individual data sets. Thecomparison and validation may be repeated many times to train andvalidate the model and to choose an appropriate model, which is thenused as a model for evaluating risk of a developing diabetes.

Use of a Scoring Model

While the Examples taught herein provide models for carrying out theinvention, the skilled artisan will appreciate that, using thebiomarkers identified herein and the relationships taught herein, anymathematical model using the biomarker panels taught herein canoptionally be used to evaluate diabetes risk.

For example, a mathematical model is provided that can calculate a riskscore based on an input of one or more biomarkers of a panel taughtherein. Biomarker level data is obtained from a subject and optionallystored on non-volatile memory). The subject biomarker data may beinitially derived through a variety of means, including self-reports,physical examination, laboratory testing and existing medical records,charts or databases. The subject biomarker level may be prepared usingcalculations, transforms, logs, combinations, normalization, etc. asneeded according to the model type selected and trained (e.g. asdetailed in the methods of developing a scoring model taught herein).Once the data has been prepared, the subject biomarker data can be inputinto the model. The model can then outputs a risk score (e.g. whereinthe risk score is indicative of likelihood to develop diabetes, relativerisk, or predicted time to diabetes contraction, etc.). The diabetespredicted by the score and other evaluation steps include, for example,type II Diabetes Mellitus and other diabetic conditions and pre-diabeticconditions.

Treatment

In one embodiment, a method of the invention comprises treating anevaluated subject, such as a subject that has been identified as havingan increased risk (e.g. at risk or high risk) for developing diabetes.

Treatments useful in the present invention can include, for example,exercise regimens, dietary modification, metabolic surgery,administration of pharmaceuticals, and any treatment known for diabetes.Examples of dietary modification include a reduction of dailycarbohydrate intake, very low carbohydrate diet (‘VLCD’, carbohydrateintake <20 g/day)) and calorie restriction. Examples of metabolicsurgery include bariatric surgery, gastric bypass, biliopancreaticdiversion, duodenal switch, and Roux-en-Y gastric bypass.

Optionally the treatment comprises administering an anti-diabetic agent.Optionally, the anti-diabetic agent is selected from an insulinsensitizer (e.g. a biguanidine), a peroxisome proliferator-activatedreceptor (‘PPAR’) activator (e.g. a thiazolidinedione), a bile acid, abile acid sequesterant (e.g. colesevelam), a DPP-4 inhibitor, a GLP-1, aGLP-1 receptor agonist, a TGR5 agonist, a sulfonylurea (e.g.glibenclamide), a meglitinide, a Sodium-glucose co-transporter 2(‘SGLT2’) inhibitor, an alpha-glucosidase inhibitor, a dopamine agonist(e.g. cycloset), an amylin mimetic (e.g. pramlintide), and an insulin oran analog or derivative thereof.

Optionally, the treatment comprises an anti-diabetic pharmaceutical,such as a small molecule. Examples include a biguanidine such asmetformin or a thiazolidinedione such as rosiglitazone or pioglitazone.

Optionally, the treatment comprises administering a bile acid such asany of GHDCA, THDCA, HDCA, HCA, GHCA, THCA, or a derivative thereof.Such treatment agents are optionally administered orally orintravenously. Optionally, a treatment administered orally is formulatedas a tablet, a capsule, a granule, or a dietary supplement. Other usefulbile acids and bile acid derivatives are disclosed in U.S. Pat. No.6,060,465 to Miljkovic et al, EP 0417725 A2 to Kramer et al, and U.S.Pat. No. 8,445,472 to Pellicciari. Other useful bile acids are those ofFormula I and I taught herein. Other useful bile acids are any steroidcarboxylic acids derived from cholesterol which bind or modulate (e.g.are an agonist of) G protein-coupled bile acid receptor 1 (‘TGR5’) orinduce the production of Glucagon-like peptide-1 (‘GLP-1), includingsalts or conjugates thereof. For example, naturally occurring bile acidsare often conjugated with glycine or taurine.

Optionally, the treatment comprises administering a compound of FormulaI. Optionally R1 is selected from α-OH and β-O(CH2)nOH where n=1-10.Optionally, R2 is selected from α-OH, α-(CH2)nCH3 where n=0-6, or—O(CH2)nCH3 where n=0-6. Optionally, R3 is selected from α-OH and H.Optionally, R4 is selected from H and (CH2)nCH3 where n=0-6. Optionally,R5 is selected from —OH, NH(CH2)COOH, NH(CH2)2SO3H, O(CH2)nCH3 wheren=0-1, or NH(CH2)nCO2Et where n=1-10. Optionally, R1-R5 are selectedsuch that the compound is not a bile acid naturally occurring in ahuman. For example, HDCA, GHDCA, and THDCA all share the same R1=α-OH,R2=α-OH, R3=H, R4=H, wherein R5 is OH in HDCA, R5 is NH(CH2)COOH inGHDCA, and R5 is NH(CH2)2SO3H in THDCA. Alternatively R1-R5 can be anysubstituents.

Optionally, the treatment comprises administering a compound of FormulaII. Optionally R1-R5 and R7-R9 R1, R2, R3, R4, and R5 are independentlyhydrogen or XL where X is nothing, O, S, NH or NL and L is hydrogen,metallic ion, halogen, an alkyl or alkenyl radical having up to 10carbon atoms, which is branched or unbranched, a cycloalkyl radicalhaving 3 to 8 carbon atoms, or a benzyl radical which is unsubstitutedor substituted 1 to 3 times by F, Cl, Br, (C₁-C₄)-alkyl or(C₁-C₄)-alkoxy; R6 is (CH2)n where 0≤n≤5, and where L is bonded to R1, Lcan alternatively be an amino acid. Optionally R1 is selected from α-OHand β-O(CH2)nOH where n=1-10. Optionally, R2 is selected from α-OH,α-(CH2)nCH3 where n=0-6, or —O(CH2)nCH3 where n=0-6. Optionally, R3 isselected from α-OH and H. Optionally, R4 is selected from H and(CH2)nCH3 where n=0-6. Optionally, R5 is selected from OH, NH(CH2)COOH,NH(CH2)2SO3H, O(CH2)nCH3 where n=0-1, or NH(CH2)nCO2Et where n=1-10.Optionally, R6 is selected from (CH2)n where n=1-5. Optionally, R7 isselected from H, C₁-C₃ alkyl, and OR10. Optionally, R8 is selected fromH, C₁-C₃ alkyl, and OR11. Optionally, R9 is selected from H, C1-C₃alkyl, and OR12. Optionally, R10-R12 are each selected from H and C1-C₃alkyl. Alternatively R1-R12 can be any substituents.

The invention also contemplates a salt, solvate, hydrate, or prodrug ofa compound of Formula I or II or a treatment comprising such.

The invention also contemplates compounds and treatments comprising acompound of Formula I or II conjugated to another compound (e.g. aminoacid such as glycine or taurine), for example, wherein the conjugate isformed at R5, where R5 comprises O, N, or S, or wherein the conjugate isformed at an O, N, or S replacing a ring hydrogen of Formula I or II.Optionally, a compound of Formula I or II is selected that increase theproduction of GLP1, e.g. in a TGR5 independent manner. Any example of anassay for such is detailed in Example 20.

Optionally, the treatment comprises administering a combination ofagents, for example, a bile acid (e.g. HDCA and/or HCA) or analogthereof, and another anti-diabetic agent taught herein (e.g. metforminor a thiazolidinedione).

Optionally, the treatment comprises administering a dipeptidylpeptidase-4 (‘DPP-4’) inhibitor. Optionally the DPP-4 inhibitor isselected from sitagliptin, vildagliptin, saxagliptin, linagliptin,gemigliptin, anagliptin, teneligliptin, alogliptin, trelagliptin,dutogliptin, omarigliptin (mk-3102), berberine, and lupeol.

Optionally, the treatment comprises administering a glucagon-likepeptide-1 (‘GLP-1’) receptor agonist. Optionally the GLP-1 receptoragonist is selected from exenatide, liraglutide, lixisenatide,albiglutide, and dulaglutide.

Optionally, the treatment comprises administering a GLP-1. Examples ofuseful GLP-1 peptides include native GLP-1 (e.g. human) or GLP-1mimetics such as those taught by Gupta (Gupta V. Glucagon-like peptide-1analogues: An overview. Indian Journal of Endocrinology and Metabolism.2013; 17(3):413-421. doi:10.4103/2230-8210.111625), U.S. Pat. No.5,545,618 to Buckley et al, and U.S. Pat. No. 6,458,924 to Knudsen etal.

Optionally, the treatment comprises administering a TGR5 agonist.Examples of useful TGR5 agonists include 1,4-diazepan-2-one compoundsand compounds taught in WO 2016/149628A1 to Kasatkin et al, Russianpatent RU2543485C2 (Grant date 10 Mar. 2015), and US 2013/0085157 A1 toSmith et al. Other useful TGR5 agonists include 2-thio-imidazolederivatives such as the compound 6g (Discovery of a Potent and OrallyEfficacious TGR5 Receptor Agonist. Agarwal et al. Desai ACS MedicinalChemistry Letters 2016 7 (1), 51-5), and RDX98940.

Optionally, the treatment comprises administering a SGLT2. Optionally,the SGLT2 is selected from empagliflozin, canagliflozin, dapagliflozin,and ipragliflozin.

Optionally, the treatment comprises administering an alpha-glucosidaseinhibitor. Optionally, the alpha-glucosidase inhibitor is selected fromAcarbose, Miglitol, and Voglibose.

Optionally, the treatment comprises administering a bile acidsequesterant, for example, cholestyramine, colesevelam, or colestipol.

Other useful treatments include administering any agent taught in WO2016094729A1 (Boehm et al.), US 20130196898 A1 (Dugi et al.), or U.S.Pat. No. 8,513,264 B2 (Mark et al.).

Optionally, the treatment is any standard of care for a diabeticindividual, e.g. a diabetic individual with type 2 diabetes.

Without being bound by theory, the present inventor believes that thetreatments taught herein can be prescribed or administered in atherapeutically effective amount that will delay, reduce, or prevent theonset of diabetes, and optionally modify the biomarker levels in thesubject to lower the diabetes risk of the subject according to ameasured biomarker level or calculated score based on measured biomarkerlevels. For example, the inventor conducted a study, from which it wasdiscovered that a VLCD reduces the P/S ratio and serum triglycerides insubjects that are at risk for developing diabetes. Biomarkers that canbe measured to demonstrate a therapeutic effect include biomarkers ofthe present invention or a panel thereof, or any known biomarker ofdiabetes status such as of fasting blood glucose, insulin, HBA1c, GLP1,or TGR5.

While the present invention contemplates the treatment of subject inwhich biomarkers of the present invention have been measured, theinvention also contemplates the use of any of the treatments disclosedherein, even outside of the diagnostic methods of the invention. Forexample, a method of treatment disclosed herein can be used following adiagnostic method of the invention or without performing a diagnosticmethod of the invention. For example, the inventor believes that certaintherapeutic methods disclosed herein such as the administration of HDCAand/or HCA or analog or derivative thereof are not known in the priorart and provide novel methods of treating a subject having diabetes,pre-diabetes, a pre-diabetic condition or a subject at-risk fordeveloping any of such. Further, the invention contemplates a compoundper se of any compounds of Formula I or II (e.g. irrespective of amethod of using the compound).

Monitoring

In one embodiment, a method of the invention comprises monitoring thelevel of one or more biomarkers. A method of monitoring according to thepresent invention comprises providing a first biological sample and asecond sample, wherein the first sample is obtained from the subject ata first time and the second biological sample is obtained from thesubject at a second time, wherein the second time is later than thefirst time, measuring the level of the one or more biomarkers in each ofthe first sample and second sample, and either comparing the measuredlevels from the first sample to the measured levels of the secondsample, or correlating the levels measured in each sample with diabetesrisk and comparing the diabetes risk correlated from the first sample tothe diabetes risk correlated from the second sample.

Optionally, the first time is prior to the subject receiving a treatmentand the second time is following the subject receiving the treatment.Accordingly, measuring the levels of biomarkers according to the presentinvention optionally further allows for the diabetes risk of a subjectto be monitored or a course of treatment to be monitored.

Optionally, the treatment is modified if the comparison of the levels ofthe one or more biomarkers from the first sample and second sample donot indicate a predetermined (e.g. substantial) reduction in diabetesrisk or do not indicate a predetermined (e.g. substantial) change in thelevel of the one or more biomarker levels.

Accordingly, identifying a subject having increased risk for developingdiabetes can be used, e.g. to enable the selection and initiation ofvarious treatment regimens (or therapeutic interventions) in order todelay, reduce or prevent the onset of type 2 diabetes in the subject.Measuring the levels of biomarkers optionally further allows for thecourse of treatment to be monitored. In this method, a first biologicalsample can be provided from the subject prior to undergoing treatmentand a second biological sample can be provided from the subjectfollowing the treatment.

Kits

In one embodiment, a method of the invention provides a kit fordetecting one or more biomarkers of a panel of the invention. Includedin the kit are one or more internal standards for detecting one or morebiomarkers of a panel of the invention. Collectively, the one or moreinternal standards provide at least one internal standard for eachbiomarker of the panel.

Optionally, the kit comprises a container, a filter, or both (e.g. afilter in a container). Optionally the container comprises the one ormore internal standards. Additionally or alternatively, a filtercomprises the one or more internal standards. Examples of such kits aredepicted in FIGS. 9A, 9B, and 10.

Optionally, the internal standards configured for GC-MS or LC-MS.

Optionally, the one or more internal standards are a plurality ofinternal standards. Optionally, the internal standards are provided in amixture. Alternatively, one or more of the plurality of internalstandards can be separated from each other.

Optionally, optionally, the one or more internal standards are providedin solid or liquid form. Optionally, the one or more internal standardsdehydrated or freeze-dried (e.g. by lyophilization). Solid internalstandards can be, for example, suspended into solution prior to use ofthe kit.

Optionally, the internal standards are any taught herein.

Optionally, the one or more internal standards comprise a labeledsteroid acid (e.g. bile acid), a labeled fatty acid, and/or a labeledamino acid. The label can be, e.g. an isotope such as (2)H or (13)C.

Optionally, each internal standard is the same compound as acorresponding biomarker of the panel, except it has one or more of itsatoms replaced with a stable isotope of the one or more atoms (e.g.(2)H, (13)C, (15)N, or (18)O). For example, a set of internal standardsfor a given panel of biomarkers can be provided by providing an isotopelabeled variant of each biomarker. Optionally, the panel is any panelselected from the panels A-BZ listed in FIGS. 12A-12C.

Optionally, the kit comprises a filter. Optionally, the one or moreinternal standards are deposited on the filter (e.g. as depicted in FIG.9 and FIG. 10). Optionally, the filter is provided in a container or isconfigured for placement in or on the container (e.g. as depicted inFIG. 9 and FIG. 10). Optionally, the filter is removable from thecontainer (e.g. as depicted in FIG. 9A). Optionally, the filter ismounted to a filter holder, e.g. a filter holder that can be placed in acontainer (e.g. a cylindrical filter holder and/or a filter holderhaving a lip as depicted in FIG. 9 and FIG. 10), for example a filterholder that is itself a container (with solid side walls) that can beplaced inside another container, e.g. as depicted in FIG. 9A.Optionally, when the filter is in the container, the container can holda volume of liquid on each side of the filter, e.g. by providing a voidor cavity on each side of the filter (e.g. as depicted in FIG. 9B). Sucha kit optionally allows the container to be centrifuged to force asolution from a first side of the filter through the filter to a secondside of the filter.

The filter can be configured such that the filtrate includes theinternal standards and biomarkers supplemented to the first side of thefilter. The filtrate can then be analyzed (e.g. via GCMS or LCMS) tomeasure the biomarkers and internal standards, e.g. by removing thefilter. Optionally, the filter is any filter with a pore size thatallows the passage of biomarkers of the panel and internal standards topass through but retains other components such as proteins (e.g.precipitated proteins). For example, the filter can be a 0.22 μm filter.Optionally, the filter is a Polyvinylidene Fluoride, cellulose (e.g.Cellulose acetate), or nylon filter. Optionally, the filter comprises anantioxidant such as butylated hydroxytoluene (BHT).

Optionally, the one more internal standards are mixed with anantioxidant such butylated hydroxytoluene (BHT). Such an anti-oxidantcan be provided, e.g. to product internal standards such as fatty acidsfrom degradation, thus extending the shelf life of the kit.

Optionally, the kit comprises at least container comprising the one ormore internal standards therein. Optionally, the container is a tube,vial or multi-welled or multi-chambered plate. The kit may have a singlecontainer (e.g. well or chamber), or may have multiple containers (e.g.wells or chambers). For example, the kit can comprise a multi-welledplate (e.g., a microtiter plate such as a 96-well microtiter plate).Other analogous containers are also contemplated. In some kits, thecontainer may be appropriate for use in measurement of the internalstandards and quantitation of the one or more metabolites to be assessedin a subject sample. In some kits, the container used for measurement ofinternal standards and quantitation of one or more metabolites in asubject sample is configured to be used for spectral analysis such as,for example, chromatography-mass spectrometry. For example, thecontainer may be configured for GC-TOFMS and/or LC-TQMS. In other kits,the container may be configured for other analytical tests specific forone or more of the metabolites to be assessed in a subject sample (e.g.,enzymatic, chemical, colorimetric, fluorometric, etc.).

Some kits include a plurality of containers. For example, some kitsinclude one or more containers having the internal standards. Inaddition, some kits include one or more containers having the internalstandards and an additional container to be used in measurement of theinternal standards and quantitation of the one or more metabolites to beassessed in a subject sample (e.g., a multi-welled plate or another tubeor vial). In some kits, there is a single container that is used tocontain the one or more internal standards and used in measurement ofthe internal standards and quantitation of the one or more metabolitesto be assessed in a subject sample.

In kits comprising a multi-welled, multi-chambered, or othermulti-container device, the internal standards may optionally be locatedin one or more wells or chambers upon distribution of the kit for use.In some kits, the internal standards are provided outside of thecontainer and must be dispersed into the container(s) while using thekits.

The container of the kit can also be configured to accept a biologicalsample from at least one subject. For example, where the kit includesmultiple chambers or wells, a biological sample from a subject may bedistributed into one or more chambers or wells. In some instances, oneor more amounts of a subject sample may be distributed into a pluralityof chambers or wells. The container of the kit is generally configuredto accept fluid samples (e.g., fluid biological samples or solidbiological samples that have been processed to obtain a fluid foranalysis).

Some kits also include reagents useful for measurement of the internalstandards and quantitation of the one or more metabolites to be assessedin a subject sample. These reagents may be included in the kit in one ormore additional containers.

An examplary kit may comprise an internal standard each provided in thesame or a separate container. The container is optionally provided inthe form of a microtiter plate, e.g. configured for use with either aGC-MS or LC-MS device. The microtiter plate will have a sufficientnumber of wells to receive at least one internal standard. The internalstandards have known concentrations and will be already in the containeror used to dispense a known amount of each internal standard intoseparate wells of the microtiter plate. After dispensing the internalstandards into the analytical container, a portion of the subject samplecan also be dispensed into the microtiter plate. Either a single portionof a subject sample is dispensed or a plurality of portions can bedispensed. If a plurality of portions is dispensed into the microtiterplate, each portion may be dispensed into a separate well. In addition,if a plurality of portions is dispensed into the microtiter plate, eachportion may be of a different amount.

Computers and Modules and Automated Systems

Methods of the invention can be implemented through the use of acomputer configured to perform one or more steps of measuring,correlating, and reporting. Accordingly, one embodiment of the presentinvention provides a non-volatile memory comprising a module configuredfor measurement (e.g. converting measurement signals from an analyticalmachine into biomarker levels), evaluating (e.g. correlating thebiomarker levels with diabetes risk or inputting the biomarker levelsinto a mathematical model that computes a score), and/or reporting theresult of the evaluation. Optionally, the invention provides a computercomprising a microprocessor and the non-volatile memory, wherein themicroprocessor is configured to carry out the module.

The steps of measurement and/or evaluating (e.g. correlating, comparingvalues or a calculating score), can be performed using a computercomprising a module there for (e.g. program stored on non-volatilememory and carried out by a microprocessor). For example, a measurementmodule can be provided that interprets a signal indicative of biomarkerlevel from a connected measuring device (e.g. ‘MS’) and calculates thelevel of the of the biomarker there from. Optionally, the measurementmodule is configured to normalize the level of the biomarker bycomparing the signal to a signal obtained (e.g. via MS) from arespective internal standard. As another example, an evaluation modulecan be provided that makes a determination of diabetes risk using thebiomarker level as an input into an algorithm (e.g. an algorithm thatcomputes risk score or likelihood of developing diabetes or thatcompares biomarker levels to comparator levels).

Optionally, the module is configured to report the results of theevaluation. Examples of reporting mechanisms include visible display, alink to a data structure or database, or a printer. The reporting meanscan optionally be a data link to send test results to an externaldevice, such as a data structure, data base, visual display, or printer.

Methods of the present invention can be automated using diagnostic testsystems that utilize a computer or an analog machine. Tests to measurebiomarkers and biomarker panels can be implemented on a wide variety ofdiagnostic test systems. Diagnostic test systems can be apparatuses thattypically include means for obtaining test results from biologicalsamples. Examples of such means include modules that automate thetesting (e.g., detection assays). Diagnostic test systems can optionallybe configured to handle multiple biological samples and can beprogrammed to run the same or different tests on each sample. Diagnostictest systems optionally include means for collecting, storing and/ortracking test results for each sample, usually in a data structure ordatabase. Examples include physical and non-volatile storage devices(e.g., hard drives, flash memory, magnetic tape, or paper print-outs).Optionally, diagnostic test systems a means for reporting test results.Examples of reporting means include visible display, a link to a datastructure or database, or a printer. The reporting means can optionallybe a data link to send test results to an external device, such as adata structure, data base, visual display, or printer.

Biomarkers

According to the present invention, a panel comprising one or morebiomarkers is selected and used for a method or kit of the invention.

Optionally, the panel comprises one or more biomarkers of FIG. 11.

Optionally, the panel is selected from panels A-BZ listed in FIG.12A-12C. The biomarkers present in each panel or indicated by an “x”.For example, Panel A includes GHDCA (or % GHDCA) and the ratio ofC16:0/C18:0 fatty acids. Other useful panels can be produced by addingan additional biomarker (e.g. any listed in FIG. 11) to any of panelsA-BZ.

A biomarker selected for level measurement of level can optionally be ananalyte (e.g. palmitic acid (‘P’) or stearic acid (S’)) such as theabsolute level of the analyte or it can be a calculation based on aplurality of analyte levels. For example, the calculation can be a ratioof analytes (e.g. ratio of P/S) or a relative level (e.g. percent) ofone or more analytes compared to one or more analytes (e.g. % GHDCA oftotal bile acids) or the sum of analyte levels (e.g. totaltriglycerides). When a first biomarker is calculated as a ratio of asecond biomarker to a third biomarker, the first biomarker level can bemeasured or determined by measuring the level of the second biomarkerand the third biomarker and calculating the ratio from the measuredlevels of the second biomarker and the third biomarker. Optionally apanel biomarker is provided by the sum of the amount or quantity of atleast two biomarker taught herein (e.g. GHDCA+GHCA or % GHDCA+% GHCA orsum of all BCAAs).

Optionally, a panel of biomarkers according to the present inventioncomprises 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers, or more.

In any aspect of the invention, the panel optionally comprises one ormore bile acids, one or more amino acids, one or more free fatty acids,and/or one or more one or more blood biochemical indices.

Optionally, the one or more bile acid biomarkers are selected fromGlycohyodeoxycholic acid (‘GHDCA’), percent GHDCA (% GHDCA),Taurohyodeoxycholic acid (‘THDCA’), percent THDCA (‘% THDCA’),Hyodeoxycholic acid (‘HDCA’), percent HDCA (‘% HDCA’),Taurochenodeoxycholic acid (‘TCDCA’), Taurodeoxycholic acid (‘TDCA’),Glycohyocholic acid (‘GHCA’), percent GHCA (% GHCA), Hyocholic acid(‘HCA’), percent HCA (‘% HCA’), Taurohyocholic acid (‘THCA’), andpercent THCA (‘% THCA’), and Taurolithocholic acid (‘TLCA’). A low orlevel decreased level (e.g. relative to a subject that did not laterdevelop diabetes, or relative to a control or comparator) of any ofthese bile acids can contribute to an increased risk of developingdiabetes. According to the present invention, the percent of a bile acid(e.g. % GHDCA) is determined with respect to all bile acids present inthe sample, for example percent by mass or percent by number ofmolecules. Optionally, when a panel comprises a plurality of bile acids,each of the bile acids is measured as percent bile acid or each of thebile acids is measured as the absolute level of bile acid.

Optionally, the one or more one or more amino acids comprise one or morebranch chain amino acids (‘BCAAs’) and/or one or more aromatic aminoacids (‘AAAs’). Optionally, the BCAAs comprises one or more of leucine,isoleucine, and valine. Optionally, the AAAs comprises phenylalanineand/or tyrosine.

Optionally, the one or more one or more amino acids comprise one or moreof Alanine, Aspartic acid, Beta-Alanine, Creatine, Cystine, Glycine,Histidine, Isoleucine, Leucine, Methionine, N-Acetyl-L-aspartic acid,Proline, Pyroglutamic acid, Serine, S-Methyl-cysteine, Threonine,Tryptophan, Tyrosine, Valine, and Phenylalnine.

Optionally, the one or more one or more free fatty acids comprises oneor more of Lauric acid (C12:0), Myristic acid (C14:0),12-Methyltridecanoic acid (C14:0 iso), Myristoleic acid (C14:1 n5),13-Methylmyristic acid (C15:0 iso), Pentadecanoic acid (C15:0), Palmiticacid (C16:0), 14-methylpentadecanoic acid (C16:0 iso), Palmitoleic acid(C16:1 n7), Palmitelaidic acid (C16:1 t9), 15-Methylpalmitic acid (C17:0iso), Margaric acid (C17:0), Stearic acid (C18:0), 16-Methylmargaricacid (C18:0 iso), Oleic acid (C18:1 n9), Elaidic acid (C18:1 t9),Linoleic acid (C18:2 n6), α-Linolenic acid (C18:3 n3), Nonadecanoic acid(C19:0), Nonadecenoic acid (C19:1 n9), Eicosenoic acid (C20:1 n9),dihomo-γ-linolenic acid (C20:3 n6), Arachidonic acid (C20:4 n6), Erucicacid (C22:1 n9), Docosatetraenoic acid (C22:4 n-6), Docosapentaenoicacid (C22:5 n-6), Eicosenoic acid (C20:1 n-9), C16:0/C18:0 ratio, C18:1n9/C18:0 ratio, and C20:3 n6/C20:4.

Optionally, the one or more blood biochemical comprise one or more oftotal Triglycerides (TG), Gemoglobin A1c (HBA1c), Glucose, Insulin, Highdensity lipoprotein (HDL), and Low density lipoprotein (LDL).

Optionally, the panel comprises:

-   -   a. one or more (e.g. each) of GHDCA or % GHDCA, THDCA or %        THDCA, HDCA or % HDCA, HCA or % HCA, GHCA or % GHCA, and THCA or        % THCA;    -   b. one or more (e.g. each) of Palmitic acid (C16:0), Stearic        acid (C18:0), Oleic acid (C18:1 n9), dihomo-γ-linolenic acid        (C20:3 n6), and Arachidonic acid (C20:4 n6);    -   c. one or more (e.g. each) of Isoleucine, Leucine, Tyrosine,        Valine, and Phenylalanine;    -   d. TG;    -   e. a combination of (a) and (b), (a) and (c), (a) and (d), (b)        and (c), (b) and (d), or (c) and (d);    -   f. a combination (a), (b), and (c);    -   g. a combination (a), (b), and (d);    -   h. a combination (a), (c), and (d); or    -   i. a combination (b), (c), and (d).

Optionally, the panel comprises a panel selected from panels A-BZ andfurther comprises HBA1c.

Optionally, the panel comprises a panel selected from panels A-BZ andfurther comprises total BCAA's.

Optionally, the panel comprises a panel selected from panels A-BZ andfurther comprises Tyrosine, Valine, or both.

Optionally, the panel comprises a panel selected from panels A-BZ andfurther comprises TG.

Optionally, the panel comprises a panel selected from panels A-BZ andfurther comprises C16:0/C18:0 ratio.

Through insight of the inventor and/or through discoveries detailed inthe examples taught herein, the biomarkers listed in FIG. 11 and panelsA-BZ listed in FIG. 12A-12C have been determined to provide diagnosticability for evaluating diabetes risk. One skilled in the art can usebiomarkers and biomarker panels taught herein to obtain diabetes riskcorrelations based there on, for example, scoring models based on atraining set of individuals who did and did not develop diabetes over aperiod of time. While some biomarkers taught herein have previously beencorrelated with diabetes or diabetes risk, the inventor believes thatthe addition of certain other biomarkers which have not been previouslylinked diabetes or diabetes risk add substantial diagnostic power tosuch previously known biomarkers, thereby providing panels of theinvention with a high capacity to identify subjects at risk fordeveloping diabetes.

EXAMPLES Example 1 Study Design and Population

Studies took place at the Shanghai Jiao Tong University Affiliated SixthPeople's Hospital. The study protocols were approved by the local ethicscommittees. Written, informed consent was obtained from allparticipants.

Sample Set 1: A total of 312 subjects were selected from the ShanghaiObesity Study (SHOS) and enrolled in a cross-sectional study. The SHOSwas a prospective study designed to investigate the occurrence anddevelopment of diabetes and its related diseases. Beginning in 2009, theSHOS recruited 5000 participants from four communities in Shanghai,China, which included a baseline study as well as, 1.5-, 3-, and 5-yrfollow-up studies. Of the 312 subjects in the cohort, 132 healthysubjects were normal weight (NW), 107 subjects were either overweight orobese (HO), and 73 subjects had been diagnosed with T2D complicated withhypertension, high cholesterol or hypertriglyceridemia (UO). Theclinical characteristic of the populations is shown in Table 1.

Sample Set 2: 10-year longitudinal study: 62 subjects were selected fromthe Shanghai Diabetes Study (SHDS). The SHDS cohort was a multi-stagestratified epidemiological study designed to assess the prevalence ofdiabetes and associated metabolic disorders. It was initiated in 1998,when 5,994 individuals were enrolled from two urban communities, Huayangand Caoyang Districts in Shanghai, China, and 1,250 of them completedthe follow-up examination in Huanyang District between 2010 and 2011.Among 1,250 eligible participants, we selected 62 subjects who wereoverweight/obese and metabolically healthy at baseline, of which, 50became unhealthy overweight/obese with diabetes (UO) and 12 remainedhealthy overweight/obese (HO) after ten years. The clinicalcharacteristic of the populations is shown in Table 2.

TABLE 1 Clinical characteristics of NW, HO and UO subjects in the cross-sectional study. NW HO UO P1 P2 P3 Male (Female) 47(85) 39(68) 37(36) 2h-glucose  5.55 ± 5.74 ± 0.1 14.88 ± 1.52E− 3.76E− 8.69E− (mmol/L) 0.090.5 01 32 30 Fasting glucose    5 ± 0.04 5.14 ± 8.04 ± 0.3 1.55E− 1.25E−2.56E− (mmol/L) 0.03 03 28 25 0.5 h-glucose  8.18 ± 8.55 ± 13.34 ±3.42E− 1.49E− 5.71E− (mmol/L) 0.14 0.14 0.41 02 24 22 TG (mmol/L)  0.82± 0.99 ±  2.38 ± 4.99E− 9.26E− 1.31E− 0.02 0.03 0.17 05 26 18 TC(mmol/L)  4.28 ± 4.26 ±  5.44 ± 6.82E− 1.64E− 1.93E− 0.05 0.05 0.11 0116 16 Age (years) 45.721 ±  46.269 ±  57.111 ±  7.72E− 1.42E− 9.01E−0.852 0.788 0.846 01 14 15 SBP (mmHg) 114.61 ±  116.01 ±  137.54 ± 3.48E− 1.16E− 4.48E− 0.95 1.06 2.57 01 16 14 HOMA-IR  1.3 ± 0.05 2.09 ±0.1  3.89 ± 5.32E− 2.32E− 6.30E− 0.24 11 25 12 HOMA-β  80.4 ± 115.8 ± 61.64 ± 5.96E− 2.60E− 8.91E− 3.16 6.48 4.21 07 04 12 0.5 h-insulin 50.17± 67.75 ±  38.76 ± 1.80E− 3.62E− 7.89E− (μU/ml) 2.14 3.51 3.58 05 05 112 h-insulin (μU/ml) 27.83 ± 36.82 ±  69.67 ± 1.27E− 1.34E− 1.63E− 1.472.32 5.9 03 14 08 LDL (mmol/L)  2.47 ± 2.69 ± 3.29 ± 0.1 5.51E− 3.15E−1.26E− 0.04 0.04 04 12 07 DBP (mmHg) 72.21 ± 74.6 ± 81.91 ± 5.97E−9.29E− 3.74E− 0.67 0.82 1.63 03 09 05 Waist (cm) 72.72 ± 87.9 ± 92.39 ±2.86E− 7.56E− 1.01E− 0.4 0.75 0.82 35 32 04 Uric acid 274.47 ±  297.16±  328.95 ±  3.03E− 1.13E− 1.05E− (μmol/ml) 5.17 7.29 7.78 02 08 03 HDL(mmol/L)  1.63 ±  1.4 ±  1.27 ± 3.27E− 4.53E− 1.34E− 0.03 0.03 0.03 0815 03 Fasting insulin  5.78 ± 9.07 ± 11.03 ± 5.70E− 6.25E− 8.67E−(μU/ml) 0.23 0.41 0.6 11 16 03 ALT (U/L) 15.21 ± 19.13 ±  22.92 ± 7.59E−3.62E− 1.44E− 0.6 0.81 1.19 05 09 02 BMI (kg/m²) 20.51 ± 27.11 ±  27.68± 2.78E− 2.28E− 1.37E− 0.06 0.18 0.3 40 32 01 Creatinine 65.45 ± 65.21±  64.05 ± 7.60E− 4.35E− 6.12E− 1.2 1.37 1.72 01 01 01 AST (U/L) 19.7 ±0.4 20.27 ±   20.7 ± 3.49E− 2.02E− 7.02E− 0.5 0.66 01 01 01 Urea 4.87 ±0.1 4.85 ± 4.81 ± 0.1 7.58E− 7.77E− 9.97E− 0.11 01 01 01 Note: Values inTable 1 represent means ± SEM. P1, P2, P3 values are calculated usingMann-Whitney U test to compare the FFA differences between NW and HO, NWand UO, HO and UO, respectively. The variables are ordered by P3 values.Abbreviations used: 2 h INS, 2 h insulin; 2 h PG, 2 h postprandialglucose; DBP, diastolic blood pressure; FINS, fasting insulin; FPG,fasting plasma glucose; HbAlc, glycated hemoglobin A1c; HDL-C, highdensity lipoprotein cholesterol; LDL-C, low density lipoproteincholesterol; MH, metabolically healthy; NW, normal weight; OW/OB,overweight or obese; P/S, palmitic/stearic acid; SBP, systolic bloodpressure; T2D, type 2 diabetes; TC, total cholesterol; TG,triglycerides.

TABLE 2 The baseline clinical characteristics of participants in thelongitudinal study with 10-year follow-up. HO UO FC P1 Male (Female)1(11) 15(35) Age (years) 39.92 ± 3.66  43.94 ± 1.83  1.02 5.80E−01 BMI(kg/m²) 26.88 ± 0.47  26.89 ± 0.37  0.99 4.87E−01 SBP (mmHg) 109.22 ±3.78  115.57 ± 1.7   1.05 3.74E−01 DBP (mmHg) 72.31 ± 2.55  74.33 ±0.77  1 7.87E−01 Fasting glucose 4.87 ± 0.12  4.7 ± 0.06 0.96 2.30E−01(mmol/L) 2 h-glucose  5.1 ± 0.19 5.26 ± 0.16 0.99 7.01E−01 (mmol/L)Fasting insulin  7.4 ± 0.85  7.1 ± 0.48 0.85 4.65E−01 (μU/ml) 2h-insulin  43.6 ± 11.43 39.42 ± 3.51  0.91 9.50E−01 (μU/ml) HOMA-IR 1.61± 0.19 1.48 ± 0.1  0.83 3.83E−01 TG (mmol/l) 0.94 ± 0.09 1.14 ± 0.041.28 4.99E−02 TC (mmol/l) 4.17 ± 0.13 4.08 ± 0.07 0.93 4.98E−01 HDL(mmol/l) 1.31 ± 0.06 1.34 ± 0.03 1.01 5.09E−01 LDL (mmol/l) 2.74 ± 0.132.67 ± 0.06 0.92 5.04E−01 Note: Values in Table 2 represent means ± SEM.FC values are fold changes ratios of medians in UO over HO group. P1values are calculated using Mann Whitney-U test, and highlighted in boldif p < 0.05. OR (95% CI) are odd ratios (95% confidence intervals) formetabolic syndrome from logistic regression models. These models areadjusted for age, sex, BMI, HOMA-IR, and fasting glucose. P2 values arecalculated from logistic regression models.

Diagnosis Criteria

A cutoff point of BMI 28 kg/m² was used to define obesity (≥28 kg/m²), aBMI of 24 kg/m2 was used to define overweight (≥24 kg/m²) and normalweight was defined as (<24 kg/m²) based on the recommendation ofoverweight and obesity for Chinese population. Zhou B. Biomed EnvironSci 2002; 15(3):245-52. Clinical characteristics and metabolic markersassociated with diabetes were examined for all the participants in thetwo independent studies in Example 1, including fasting glucose, oralglucose tolerance test (2h-glucose or OGTT), insulin level, systolic anddiastolic blood pressure (SBP and DBP), total cholesterol (TC) andtriglycerides (TG), and high-density lipoprotein and low-densitylipoprotein (HDL and LDL). “Metabolically healthy” was defined as havingall of the following: FPG ≤6.1 mmol/L, OGTT ≤7.8 mmol/L and no previoushistory of diabetes; SBP/DBP <140/90 mmHg and no previous history ofhigh blood pressure; fasting plasma TG <1.7 mmol/L and fasting plasmaHDL≥0.9 mmol/L (men) or ≥1.0 mmol/L (women), and no previous history ofhigh cholesterol (TC <5.18 mmol/L); no cardiovascular or endocrinedisease history. Pang et al., PLoS One 2014; 9(5):e97928. Failure tomeet all of the criteria was classified as “metabolically unhealthy”.

Glucose tolerance was categorized according to the American diabetesassociation criteria: normal glucose tolerance (NGT), FPG <6.1 mmol/Land 2h PG <7.8 mmol/L; two categories of impaired glucose regulation(IGR): a) impaired glucose tolerance (IGT), FPG <7.0 mmol/L and 2h PG≥7.8 and <11.1 mmol/L, and b) impaired fasting glycemia (IFG), FPG <7.0and ≥6.1 mmol/L and 2h PG <7.8); and T2D (FPG ≥7.0 mmol/L or 2h PG ≥11.1mmol/L, or antidiabetic treatment. Association American Diabetes.Diabetes Care 2004; 27:S15-35.

Anthropometric and Biochemical Measurements

Each participant had a physical examination including measurement ofheight, weight, waist circumference and blood pressure. Body mass index(BMI) was calculated as weight (kg) divided by squared height (m²).Waist circumference was measured at the horizontal plane between theinferior costal margin and the iliac crest on the mid-axillary line.Blood pressure was the average of three measurements made with asphygmomanometer at two minute intervals.

After a fasting venous blood sample was collected, each participantreceived a 75 g oral glucose tolerance test. Plasma glucose levels weremeasured by the glucose oxidase method. Serum insulin was assayed used abio-antibody technique (Linco, St Louis, Mo., USA). Serum triglyceridesand high density lipoprotein cholesterol were determined by standardcommercial methods on a parallel, multichannel analyser (Hitachi7600-020, Tokyo, Japan). An experienced technician who was blinded tothe study measured glycated haemoglobin A1c (HbA1c) using highperformance liquid chromatography (HLC-73G7, Tosoh, Japan). All of themeasurements were carried out within two hours of blood collection.Insulin resistance and β cell function were evaluated using thefollowing formulas: (1) HOMA IR (mlU·mmol/L2)=fasting insulin(mlU/L)×fasting glucose (mmol/L)/22.5; (2) HOMA-β(mlU/mmol/L)=20×fasting insulin (mlU/L)/(fasting glucose (mmol/L)−3.5).

Example 2 Targeted Metabolomics Analysis of Bile Acids (BAs)

Serum Sample Preparation.

An aliquot of 50 μl of serum was mixed with 150 μl of methanol (contains0.10 μM of cholic acid (CA)-d4, ursodeoxycholic acid (UDCA)-d4, andlithocholic acid (LCA)-d4 used as the internal standard). The mixturewas then vortexed for 2 min, allowed to stand for 10 min, and thencentrifuged at 20000 g at 4° C. for 10 min. An aliquot of 160 μLsupernatant was transferred to a clean tube and vacuum dried. Theresidue was redissolved with equal amount of acetonitrile (0.1% formicacid) and water (0.1% formic acid) to a final volume of 40 μL. Aftercentrifugation, the supernatant was used for ultra-performance liquidchromatography-triple quadrupole mass spectrometry (UPLC-TQMS) analysis.

Method Validation.

Each aliquot of 41 standard stock solutions was mixed to obtain a mixedstock solution. Calibration solutions containing all 41 BA standardswere prepared at a series of concentrations of 0.610, 1.221, 2.441,4.883, 9.766, 19.531, 39.063, 78.125, 156.250, 312.5, 625.0, 1250.000,and 2500.00 ng/mL in naïve pooled serum depleted of BAs using activatedcharcoal. The calibration curve and the corresponding regressioncoefficients were obtained by internal standard adjustment. All BAs werefound to be linear over the measured range.

Instrumentation.

Serum BAs were measured according to methods previously reported withminor modifications. Xie, et al. 2015, J Proteome Res 14 (2), 850-859. AWaters ACQUITY UPLC system equipped with a binary solvent deliverymanager and a sample manager (Waters, Milford, Mass.) was usedthroughout the study. The mass spectrometer was a Waters XEVO TQ-Sinstrument with an ESI source (Waters, Milford, Mass.). The entire LC-MSsystem was controlled by MassLynx 4.1 software. All chromatographicseparations were performed with an ACQUITY BEH C18 column (1.7 μm, 100mm×2.1 mm internal dimensions) (Waters, Milford, Mass.).

LC-MS Analysis.

The mobile phase consisted of 0.1% formic acid in LC-MS grade water(mobile phase A) and 0.1% formic acid in LC-MS grade acetonitrile(mobile phase B) run at a flow rate of 0.3 mL/min. The flow rate was0.45 mL/min with the following mobile phase gradient: 0-1 min (5% B),1-5 min (5-25% B), 5-15.5 min (25-40% B), 15.5-17.5 min (40-95% B),17.5-19 min (95% B), 19-19.5 min (95-5% B), 19.6-21 min (5% B). Thecolumn was maintained at 45° C. and the injection volume of all sampleswas 5 μl.

The mass spectrometer was operated in negative ion mode with a 1.2 kvcapillary voltage. The source and desolvation gas temperature was 150and 550° C., respectively. The data was collected with multiple reactionmonitor (MRM) and the cone and collision energy for each BA used theoptimized settings from QuanOptimize application manager (Waters Corp.,Milford, Mass.).

Data Analysis.

UPLC-TQMS raw data obtained with negative mode were analyzed usingTargetLynx applications manager version 4.1 (Waters Corp., Milford,Mass.) to obtain calibration equations and the quantitativeconcentration of each BA in the samples. A Student's t test was used toinvestigate differences between the groups in BAs measurements. Theresultant p values for all metabolites were subsequently adjusted toaccount for multiple testing by false discovery rate (FDR) method. Pike,Methods in Ecology and Evolution 2011, 2 (3), 278-282. We regarded pvalues of <0.05 as significant.

Example 3 Targeted Metabolomics Analysis of Free Fatty Acids (FFAs)

FFA Extraction.

The concentrations of free fatty acids (FFAs) were determined using thepreviously reported method with some modification. Püttmann et al., ClinChem 1993; 39(5):825-32. In brief, 10 μL of isotope labeled internalstandards (5 μg/mL of nonadecanoic-d37 acid and tridecanoic-d25 acid)was added to each 40 μL of serum sample, followed by 500 μL ofisopropyl/hexane/phosphoric acid (2M) (40/10/1 by vol). Samples werethen vortexed for 2 min. After incubating at room temperature for 20min, 400 μL of n-hexane and 300 μL of water were added, vortexed andcentrifuged at 12,000 rpm for 10 min. Then 400 μL of the upper organiclayer was transferred to a new tube and 400 μL of n-hexane was added tothe lower layer for further extraction. After vortex and centrifuge, allof the upper organic phase was then combined with the first supernatantand dried under vacuum. The residue was reconstituted with 80 μL ofmethanol and subjected to ultra-performance liquid chromatographyquadrupole-time-of-flight mass spectrometry (UPLC-TQMS) analysis.

LC-MS Conditions.

An ACQUITY-ultra-performance liquid chromatography system (WatersCorporation, Milford, USA) equipped with a binary solvent deliverysystem and an auto-sampler (Waters Corporation, Milford, USA) wasemployed for the separation on a 100 cm×2.1 mm BEH C₁₈ column with 1.7μm particles at 40° C. (Waters Corporation, Milford, USA). The optimalmobile phase consisted of water (solvent A) and acetonitrile/isopropylalcohol (80/20 by vol, solvent B) and the flow rate was set at 0.4mL/min. The injection volume was of 5 μL. A gradient elution conditionwas applied as follows: 70% B over 0˜2 min, 70%˜75% B over 2˜5 min,75%˜80% B over 5˜10 min, 80%˜90% B over 10˜13 min, 90%˜100% B over 13˜16min, maintained for 5 min, then returned to 70% B over 21˜22.5 min andre-equilibrated for 1.5 min. The mass spectrometric data was collectedusing a tandem triple quadrupole (TQ) mass spectrometry (Manchester,UK). ESI was used as the ionization source and the analysis was carriedout in the negative mode. The following parameters were used: capillaryvoltage, 2500 V; sampling cone, 55 V; extraction cone, 4V; desolvationtemperature, 450° C.; source temperature, 120° C.; desolvation gas flow,650 L/h; cone gas flow, 50 L/h; Lm resolution, 4.7; Hm resolution, 15;scan time, 0.35 s, and inter scan time 0.02 s. Leucine enkephalin wasused as the lock mass (m/z 554.2615) at a concentration of 100 ng/mL andflow rate of 0.2 mL/min, with a lockspray frequency of 20 s

Example 4 Targeted Metabolomics Analysis of Amino Acids (AAs)

The serum levels of the amino acids (AAs) in all the enrolledparticipants were analyzed by ultra-performance liquid chromatographytriple quadruple mass spectrometry (UPLC-TQMS, Waters, Milford, Mass.,USA). In brief, 10 μL of isotope labeled internal standards (5 μg/mL ofvaline-d8, leucine-5,5,5-d3, isoleucine-2-d1, tyrosine-3,3-d2, andphenylalanine-3,3-d2) were added to each of 40 μL aliquot of serumsample. After diluted with 80 μL of water, the sample was extracted with500 μL of a mixture of methanol and acetonitrile (1:9, v/v). Theextraction procedure was performed at −20° C. for 10 min after 2 minvortexing and 1 min ultrasonication. The sample was then centrifuged at4° C. at 12000 rpm for 15 min. An aliquot of 20 μL supernatant wasvacuum-dried at room temperature. After that, the residue wasredissolved by 100 μL of a mixture of methanol and water (1:1, v/v) with1 μg/mL of L-2-chlorophenylalanine followed by the same vortexing,ultrasonication and centrifugation steps ahead. A volume of 80 μLsupernatant was transferred into the sampling vial for UPLC-TQMSanalysis (Waters, Manchester, U.K). A 5 μL aliquot of sample wasinjected into an ultra-performance liquid chromatography system (Waters,U.K.) with a 4.6 mm×150 mm, 5 μm Elispse XDB-C18 column (Agilent, USA).The column was held at 40° C. The elution procedure for the column was1% for the first 0.5 min, 1-20% B over 0.5-9 min, 20-75% B over 9-11min, 75-99% B over 11-16 min, and the composition was held at 99% B for0.5 min, where A=water with 0.1% formic acid and B=acetonitrile with0.1% formic acid for positive mode (ESI+) and the flow rate was 0.4mL/min. A Waters XEVO-Triple Quadrupole MS was used for the massspectrometry detection. The temperature for the source and desolvationgas was set at 150 and 450° C. respectively. The gas flow for cone anddesolvation was 50 and 800 L/h respectively. The capillary voltage wasset to 3.0 kV. All the compounds were detected in multiple reactionmonitor (MRM) mode.

Example 5 Serum Triglyceride Analysis

The total triglycerides were determined using a serum triglyceridedetermination kit with enzymatic methods.

Example 6 Quality Controls (QCs)

Reproducibility is critical for unbiased profiling of metabolomics. Theentire process of the metabolomic analysis was subject to stringent QCto ensure the data are reproducible. Three types of QC samples,including test mixtures, internal standards, and pooled biologicalsamples, were used in the metabolomic procedures. In addition to the QCsamples, conditioning and solvent blank samples were also used forobtaining optimal instrument performance, and 5% of randomly selectedsamples were repeated for assessing reproducibility. Test mixturescomprise of a group of commercially available standards with a massrange across the system mass range. They were analyzed at the beginningand end of each batch run to ensure that the instruments perform withinthe laboratory specifications [retention time stability, peakresolution, peak signal intensity, and mass accuracy (LC-MS only)].Internal standards (chlorophenylalanine for LC-MS; chlorophenylalanineand heptadecanoic acid for GC-MS) were added to the test samples inorder to monitor analytical variations during the entire samplepreparation and analytical processes. For biomarker discovery, theinternal QC criteria/metrics are 1) coefficient of variation (CV) ≤15%within 100 injections, and 2) CV ≤20% within 300 injections. The CV isdefined as the ratio of the standard deviation to the mean peak ofsignal intensity. Pooled QC samples which combine serum aliquots fromall the study subjects (or representative subjects depending on thenumber of samples to be tested) are used for assessing the overallreproducibility and correcting inter-batch variations. The QC sampleswere prepared with the test samples and injected at regular intervals(after every 10 test samples for GC-MS and for LC-MS, respectively)throughout the analytical run.

Example 7 Statistical Analysis

The raw data produced by UPLC-MS such as UPLC-TQMS were initiallyprocessed using TargetLynx applications manager version 4.1 (WatersCorp., Milford, Mass.) to detect peak signals, obtain calibrationequations, and calculate the concentration of each metabolite. Manualexamination and correction were needed to ensure data quality. Allstatistical computing and graphics were carried out using R and SIMCA13.0.1 software (Umetrics, Sweden).

Prior to the statistical analysis, we examined the distribution of eachcontinuous variable (i.e., clinical characteristics, metabolic markersand metabolite markers) using the Shapiro-Wilk test, and found that 90%of the variables deviated from normality, thus non-parametric tests wereused for this study. We used the Mann-Whitney U test to compare eachmetabolic marker or metabolite markers between two sample sets, such asHO and UO group in the cross-sectional study. Variables with p-values<0.05 were considered statistically significant. Multivariate logisticregression models were used to estimate the relative risk of developingdiabetes at different metabolite marker levels, adjusting for age, sex,BMI and other confounding factors. Also, p values <0.05 were consideredsignificant from logistic regression analysis. We further calculated theROC curve areas of metabolite markers to evaluate their performance ofdiscriminating HO and UO group in the cross-sectional study and thepredictive power of estimating the risk of developing diabetes.

Example 8 Correlation of Biomarkers with the Future Development of Type2 Diabetes (T2D)

As depicted in Table 3, Significantly increased free fatty acids (FFAs),amino acids (AAs) and triglycerides (TG) concentrations were observed inoverweight/obese subjects with T2D (UO) in the cross-sectional study.GHDCA and % GHDCA were decreased in overweight/obese subjects with T2D.

TABLE 3 The identified metabolite biomarkers in the cross-sectionalstudy NW HO UO (n = (N = (n = 132) 107) 106) P1 P2 P3 TG 0.78 0.94 1.975.40E− 2.20E− 4.49E− (0.02) (0.03) (0.17) 05 26 19 C16:0 61.35 62.6786.2 (2) 4.64E− 7.37E− 5.39E− (1.23) (1.19) 01 19 18 C18:0 59.99 63.9772.21 2.58E− 5.82E− 1.57E− (1.04) (1.09) (1.71) 02 09 05 C18:1 n9 35.8233.47 56.59 1.27E− 6.47E− 8.10E− (1.67) (1.46) (2.36) 01 11 15 C20:3 n60.88 0.9 1.45 5.71E− 1.31E− 5.93E− (0.02) (0.02) (0.05) 01 24 22 C20:4n6 7.51 7.59 10.65 5.44E− 1.72E− 4.28E− (0.18) (0.19) (0.49) 01 10 09Valine 1.02 1.17 1.26 7.91E− 8.27E− 3.06E− (0.03) (0.02) (0.04) 06 08 02Leucine 0.01 (0) 0.01 (0) 0.02 (0) 5.21E− 1.12E− 3.37E− 04 06 02Isoleucine 0.08 (0)  0.1 (0)  0.1 (0) 8.44E− 1.94E− 3.72E− 04 06 02Phenyl- 0.06 (0) 0.07 (0) 0.07 (0) 8.79E− 2.55E− 3.69E− alanine 04 06 02Tyrosine 0.02 (0) 0.02 (0) 0.02 (0) 5.14E− 4.77E− 2.39E− 02 03 02 C16:0/1.01 0.99 1.19 1.44E− 8.10E− 4.12E− C18:0 (0.01) (0.01) (0.02) 01 13 16C18:1 n9/ 0.62 0.53 0.88 1.97E− 3.88E− 1.23E− C18:0 (0.03) (0.02) (0.03)02 05 09 C20:3 n6/ 0.11 (0) 0.12 (0) 0.14 (0) 6.39E− 9.99E− 1.70E− C20:401 06 04 GHDCA 4.35 4.86 2.26 1.81E− 7.66E− 5.76E− (0.86) (1.4) (0.78)04 02 03 GHDCA 0.68 0.71 0.41 3.87E− 4.04E− 3.27E− (percent (0.18)(0.16) (0.12) 04 02 02 %) Note: Values in Table 3 represent means ± SEM.The concentration unit of FFAs and AAs are μg/ml. The concentration unitof BAs is ng/ml. P1, P2, P3 values are calculated using Mann-Whitney Utest to compare the FFA differences between NW and HO, NW and UO, HO andUO, respectively.

Example 9 AAs Predict Future Diabetes

The 62 overweight subjects in Example 1 had normal metabolic markers atbaseline and 50 of them developed diabetes (UO) while 12 remainedhealthy (HO) according to their re-evaluation after ten years. Therewere no differences between these two groups at baseline according totheir metabolic markers. However, the baseline serum levels of five AAs,Valine, Leucine, Isoleucine, Phenylalanine, and Tyrosine weresignificantly increased in the UO group (Table 4). This result confirmedthat the baseline concentrations of these AAs are predictive of thedevelopment of future diabetes in these subjects. The predictive powerof the five AAs, Valine, Leucine, Isoleucine, Phenylalanine, andTyrosine for diabetes incident among MH-OW/OB subjects was furtherevaluated using univariate and multivariate models. Logistic regressionmodels adjusting for age, sex, BMI, HOMA-IR and fasting glucose werefitted.

Logistic regression analyses showed that baseline AAs levels was apositive predictor (adjusted odds ratio (OR) 2.53 [95% CI: 1.68-3.81],P=4.79E-03) of diabetes, independent of sex, age, BMI, HOMA-IR, HOMA-β,FPG and 2hPG (Table 4). The receiver operating characteristic (ROC)curves for the combination of the five AAs has an area under the curve(AUC) of 0.82 (95% CI: 0.65-0.91, P=2.84E-03) (FIG. 1). Notably, an AUCof 1.0 indicates perfect prediction and an AUC of 0.5 indicatesprediction equivalent to random selection.

TABLE 4 The baseline FFAs, AAs, BAs and TG levels of participants in thelongitudinal study and their ability for determining the risk ofdeveloping diabetes. Odds Ratio AUC (OR) HO UO under (95% (n = 12) (n =50) P1 ROC Sensitivity Specificity CI) P2 TG 0.88 1.13 4.99E− 0.82 0.80.583 3.03 3.31E− (0.09) (0.04) 02 (0.50- (1.09- 02 0.97) 8.41) C16:058.92 74.59 2.81E− 0.60 0.68 0.667 1.38 4.29E− (11.05) (4.69) 01 (0.39-(0.62- 01 0.81) 3.10) C18:0 54.99 63.24 7.15E− 0.54 0.64 0.583 0.754.08E− (8.64) (2.28) 01 (0.32- (0.38- 01 0.75) 1.48) C18:1 n9 16.8927.14 2.10E− 0.72 0.72 0.75 3.74 3.60E− (3.73) (1.78) 02 (0.54- (1.08-02 0.90) 9.71) C20:3 n6 1.36 2.47 1.06E− 0.74 0.6 0.833 4.33 4.67E−(0.33) (0.27) 02 (0.57- (1.02- 02 0.91) 18.36) C20:4 n6 5.63 7.25 6.26E−0.68 0.72 0.667 4.01 3.41E− (0.91) (1.62) 02 (0.48- (0.23- 01 0.88) 70)Valine 0.73 0.94 5.30E− 0.76 0.84 0.667 1.55 4.84E− (0.05) (0.16) 03(0.62- (1.00- 02 0.91) 2.38) Leucine 0.01 0.02 5.01E− 0.76 0.9 0.5835.39 4.52E− (0) (0.01) 03 (0.63- (1.04- 02 0.90) 28.06) Isoleucine 0.060.09 2.97E− 0.70 0.7 0.75 1.15 8.65E− (0.01) (0.06) 02 (0.55- (0.98- 020.86) 1.36) Phenylalanine 0.03 0.04 3.01E− 0.78 0.62 0.833 1.87 6.34E−(0) (0.02) 03 (0.64- (0.97- 02 0.91) 3.61) Tyrosine 0.01 0.02 1.11E−0.74 0.6 0.833 4.10 6.07E− (0) (0.01) 02 (0.60- (0.94- 02 0.88) 17.91)C16:0/C18:0 0.99 1.2 6.58E− 0.76 0.62 0.917 1.69 1.94E− (0.06) (0.04) 03(0.60- (1.09- 02 0.91) 2.63) C18:1 n9/C18:0 0.32 0.41 6.58E− 0.76 0.560.917 3.12 8.51E− (0.04) (0.02) 03 (0.59- (1.34- 03 0.92) 7.30) C20:3n6/C20:4 0.23 0.32 4.48E− 0.77 0.92 0.5 5.12 1.22E− (0.02) (0.01) 03(0.62- (1.43- 02 0.91) 18.37) GHDCA 5.07 3.02 1.46E− 0.80 0.9 0.833 0.358.93E− (0.01) (0) 03 (0.63- (0.16- 03 0.97) 0.77) GHDCA (percent % 0.750.45 1.77E− 0.79 0.82 0.75 0.26 1.32E− of total BAs) (0.13) (0.12) 03(0.63- (0.09- 02 0.96) 0.76) Combination of 0.28 2.07 4.33E− 0.83 0.940.75 8.78 5.06E− C16:0, C18:0, (0.51) (0.25) 04 (0.66-1) (1.92- 03 C18:1n9, C20:3 40.17) n6, C20:4 n6 Combination of 0.66 1.76 2.84E− 0.82 0.881 2.53 4.79E− Valine, Leucine, (0.22) (1.37) 03 (0.65- (1.63- 03Isoleucine, 0.91) 3.81) Phenylalanine, Tyrosine Combination of 0.46 2.46.88E− 0.82(0.67- 0.74 0.833 7.47 1.96E− C16:0/C18:0, C18:1 (0.39) (0.2)04 0.97) (2.09- 03 n9/C18:0, and 26.67) C20:3 n6/C20:4 Combination ofTG, 5.3 8.16 3.18E− 0.92 0.92 0.833 10.94 8.37E− C16:0, C18:0, (0.43)(2.62) 05 (0.84-1) (1.32- 03 C18:1 n9, 56.55) C20:3 n6, C20:4 n6,Valine, Leucine, Isoleucine, Phenylalanine, Tyrosine, and GHDCA Note:Values in Table 4 represent means ± SEM. The concentration unit of FFAsand AAs are μg/ml. The concentration unit of BAs is ng/ml. P1 values arecalculated using Mann Whitney-U test. OR (95% CI) are odd ratios (95%confidence intervals) for metabolic syndrome from logistic regressionmodels. These models are adjusted for age, sex, BMI, HOMA-IR, andfasting glucose. P2 values are calculated from logistic regressionmodels.

Example 10 FFAs Predict Future Diabetes

The 62 overweight subjects in Example 1 had normal metabolic markers atbaseline and 50 of them developed diabetes (UO) while 12 remainedhealthy (HO) according to their re-evaluation after ten years. Therewere no differences between these two groups at baseline according totheir metabolic markers. However, the baseline serum levels of fiveFFAs, Palmitic acid (C16:0), Stearic acid (C18:0), Oleic acid (C18:1n9), dihomo-γ-linolenic acid (C20:3 n6), and Arachidonic acid (C20:4 n6)were significantly increased in the UO group (Table 4). This resultfurther confirmed that the baseline concentrations of these FFAs arepredictive of the development of future diabetes in these subjects. Thepredictive power of the five FFAs, Palmitic acid (C16:0), Stearic acid(C18:0), Oleic acid (C18:1 n9), dihomo-γ-linolenic acid (C20:3 n6), andArachidonic acid (C20:4 n6) for diabetes incident among MH-OW/OBsubjects was further evaluated using univariate and multivariate models.Logistic regression models adjusting for age, sex, BMI, HOMA-IR andfasting glucose were fitted.

Logistic regression analyses showed that baseline FFA levels was apositive predictor (adjusted OR: 8.78 [95% CI: 1.92-40.17], P=4.33E-04)of T2D, independent of sex, age, BMI, HOMA-IR, HOMA-β, FPG and 2hPG(Table 4). The receiver operating characteristic (ROC) curves for thecombination of the five FFAs has an area under the curve (AUC) of 0.83(95% CI: 0.66-1.00, P=5.06E-03) (FIG. 2). Notably, an AUC of 1.0indicates perfect prediction and an AUC of 0.5 indicates predictionequivalent to random selection.

Example 11 Bile Acid Predicts Future Diabetes

The 62 overweight subjects in Example 1 had normal metabolic markers atbaseline and 50 of them developed diabetes (UO) while 12 remainedhealthy (HO) according to their re-evaluation after ten years. Therewere no differences between these two groups at baseline according totheir metabolic markers. However, the baseline serum levels ofglycohyocholic acid (GHDCA) were significantly decreased in the UO group(Table 4). This result confirmed that the baseline concentration ofGHDCA is predictive of the development of future diabetes in thesesubjects. The predictive power of GHDCA for diabetes incident amongMH-OW/OB subjects was further evaluated using univariate andmultivariate models. Logistic regression models adjusting for age, sex,BMI, HOMA-IR and fasting glucose were fitted.

Logistic regression analyses showed that baseline GHDCA level was apositive predictor (adjusted ORs 0.26 [95% CI: 0.09-0.76], P=8.93E-03)of diabetes, independent of sex, age, BMI, HOMA-IR, HOMA-β, FPG and 2hPG(Table 4). The receiver operating characteristic (ROC) curves for theGHDCA has an area under the curve (AUC) of 0.80 (95% CI: 0.63-0.97,P=1.46E-03) (FIG. 3). Notably, an AUC of 1.0 indicates perfectprediction and an AUC of 0.5 indicates prediction equivalent to randomselection.

Example 12 TG Predicts Future Diabetes

The 62 overweight subjects in Example 1 had normal metabolic markers atbaseline and 50 of them developed diabetes (UO) while 12 remainedhealthy (HO) according to their re-evaluation after ten years. Therewere no differences between these two groups at baseline according totheir metabolic markers. However, the baseline serum levels of TG weresignificantly increased in the UO group (Table 4). This result confirmedthat the baseline concentration of TG is predictive of the developmentof future diabetes in these subjects. The predictive power of TG fordiabetes incident among MH-OW/OB subjects was further evaluated usingunivariate and multivariate models. Logistic regression models adjustingfor age, sex, BMI, HOMA-IR and fasting glucose were fitted.

Logistic regression analyses showed that baseline TG levels was apositive predictor (adjusted ORs 3.03 [95% CI: 1.09-8.41], P=3.31E-02)of T2D, independent of sex, age, BMI, HOMA-IR, HOMA-β, FPG and 2hPG(Table 4). The receiver operating characteristic (ROC) curves for the TGhas an area under the curve (AUC) of 0.82 (95% CI: 0.50-0.97,P=4.99E-02) (FIG. 4). Notably, an AUC of 1.0 indicates perfectprediction and an AUC of 0.5 indicates prediction equivalent to randomselection.

Example 13 The Combination of TG, C16:0, C18:0, C18:1 n9, C20:3 n6,C20:4 n6, Valine, Leucine, Isoleucine, Phenylalanine, Tyrosine, andGHDCA Predicts Future Diabetes in Metabolic Healthy OW/OB Individuals

The predictive power of the combination of TG, C16:0, C18:0, C18:1 n9,C20:3 n6, C20:4 n6, Valine, Leucine, Isoleucine, Phenylalanine,Tyrosine, and GHDCA for diabetes incident among MH-OW/OB subjects wasfurther evaluated using univariate and multivariate models. Logisticregression analyses showed that baseline levels of the combination ofTG, C16:0, C18:0, C18:1 n9, C20:3 n6, C20:4 n6, Valine, Leucine,Isoleucine, Phenylalanine, Tyrosine, and GHDCA was a positive predictor(adjusted ORs 10.94 [95% CI: 1.32-56.55], P=8.37E-03) of diabetes,independent of sex, age, BMI, HOMA-IR, HOMA-β, FPG and 2hPG (Table 4).The receiver operating characteristic (ROC) curves for the combinationof TG, C16:0, C18:0, C18:1 n9, C20:3 n6, C20:4 n6, Valine, Leucine,Isoleucine, Phenylalanine, Tyrosine, and GHDCA has an area under thecurve (AUC) of 0.92 (95% CI: 0.84-1.00, P=3.18E-05) (FIG. 5). Notably,an AUC of 1.0 indicates perfect prediction and an AUC of 0.5 indicatesprediction equivalent to random selection.

Example 14 Bioinformatics Tools to Inform the Individuals the Risk ofDeveloping Diabetes

Potential biomarkers were initially evaluated and selected using bothunivariate and multivariate analysis methods. The univariate methodsinclude parametric statistics for normal-distributed variables (e.g.,student t test and ANOVA), and non-parametric tests for those thatfailed to follow normal distribution (e.g., Mann Whitney U test andKruskal Wallis test). Correlations between metabolites and theircapabilities to estimate the risk of developing diabetes were furtherevaluated using Pearson or Spearman coefficients, clustering, partialleast squares (PLS) methods, and logistic regression. With a list ofpotential biomarkers, a bioinformatics method was developed in thisinvention, which is based on logistical regression models, to obtain theoptimal combination of biomarkers. A panel of biomarkers includingGlycohyodeoxycholate (GHDCA), Palmitic acid (C16:0), Stearic acid(C18:0), Oleic acid (C18:1 n9), dihomo-γ-linolenic acid (C20:3 n6),Arachidonic acid (C20:4 n6), Valine, Leucine, Isoleucine, Phenylalanine,Tyrosine, and triglycerides (TG) in Table 3 are demonstrated to havepowerful prediction ability for future diabetes.

A panel of biomarkers (Table 4) that included TG, 5 FFAs includingPalmitic acid (C16:0), Stearic acid (C18:0), Oleic acid (C18:1 n9),dihomo-γ-linolenic acid (C20:3 n6), and Arachidonic acid (C20:4 n6), 5AAs including Valine, Leucine, Isoleucine, Phenylalanine, and Tyrosineand GHDCA, achieved powerful predictive performance according to ROCcurve analysis; the ROC curve area was 0.92 (95% CI: 0.84-1). Accordingto the Yuden index calculated from the ROC model, the optimal thresholdof the score for the combined biomarkers was 7.08, which can be used topredict the risk of developing diabetes. Individuals who have the scoregreater than 7.08 have high risk of developing diabetes in the future.

Example 15 Kits

An example Kit of the invention includes known amounts of isotopelabeled (e.g. C13 labeled) internal standards corresponding thebiomarker panel for non-diabetic individuals or diabetic patients to bemonitored. The kit may include a single mixture of all the internalstandards to be assessed, or may include a separate amount of eachinternal standard. The amounts of each internal standard in themetabolite profile to be assessed can be measured and used forcomparison to the corresponding amount of a corresponding biomarker in asample from a subject. Each internal standard may be in solid form or inliquid form in the distributed kits. If the internal standards are insolid form, they are to be suspended into solution prior to use of thekit. Kits optionally comprise at least one labeled variant of ametabolite biomarker selected from the metabolites listed in Table 3.

Example kits can include at least one container configured to containthe internal standards in the metabolite profile for non-diabeticindividuals to be assessed. The container may be a tube, a vial or amulti-welled or multi-chambered plate. The container may have a singlewell or chamber, or the container may have multiple wells or chambers.For example, the container may be a multi-welled plate (e.g., amicrotiter plate such as a 96-well microtiter plate). Other analogouscontainers are also appropriate. In some kits, the container may beappropriate for use in measurement of the internal standards andquantitation of the one or more biomarkers to be assessed in a subjectsample. In some kits, the container used for measurement of internalstandards and quantitation of one or more metabolites in a subjectsample is configured to be used for spectral analysis such as, forexample, chromatography-mass spectrometry. For example, the containermay be configured for GC-TOFMS and/or LC-TQMS. In other kits, thecontainer may be configured for other analytical tests specific for oneor more of the metabolites to be assessed in a subject sample (e.g.,enzymatic, chemical, colorimetric, fluorometric, etc.). The containermay be configured to hold an internal standard mixture, as set forthabove, in one or more vials or tubes, or in one or more chambers orwells. Alternatively, the container may be configured to hold thereference amount of each internal standard to be assessed separately(e.g., one internal standard per chamber or well).

Some kits include a plurality of containers. For example, some kitsinclude one or more containers having the internal standards. Inaddition, some kits include one or more containers having the internalstandards and an additional container to be used in measurement of theinternal standards and quantitation of the one or more metabolites to beassessed in a subject sample (e.g., a multi-welled plate or another tubeor vial). In some kits, there is a single container that is used tocontain the one or more internal standards and used in measurement ofthe internal standards and quantitation of the one or more metabolitesto be assessed in a subject sample.

In kits where the container is a multi-welled or multi-chamberedcontainer, the reference amounts of the metabolites to be assessed maybe located in one or more wells or chambers upon distribution of the kitfor use. In some kits, the reference amounts of the metabolites to beassessed must be dispersed into one or more wells or chambers in usingthe kits.

The container of the kit can also be configured to accept a biologicalsample from at least one subject. For example, where the container ofthe kit includes multiple chambers or wells, a biological sample from asubject may be distributed into one or more chambers or wells. In someinstances, one or more amounts of a subject sample may be distributedinto a plurality of chambers or wells. The container of the kit isgenerally configured to accept fluid samples (e.g., fluid biologicalsamples or solid biological samples that have been processed to obtain afluid for analysis).

Some kits also include reagents useful for measurement of the internalstandards and biomarkers and quantitation of the one or more metabolitesto be assessed in a subject sample. These reagents may be included inthe kit in one or more additional containers.

An example kit comprises a plurality of internal standards, eachprovided in a separate container or in the same container. Theanalytical container will be a microtiter plate configured for use witheither a GC-MS or LC-MS device. The microtiter plate will have asufficient number of wells to receive at least one internal standard.The internal standards will have known concentrations and will be usedto dispense a known amount of each internal standard into separate wellsof the microtiter plate. After dispensing the internal standards intothe analytical container, a portion of the subject sample can also bedispensed into the microtiter plate. Either a single portion of asubject sample is dispensed or a plurality of portions can be dispensed.If a plurality of portions is dispensed into the microtiter plate, eachportion may be dispensed into a separate well. In addition, if aplurality of portions is dispensed into the microtiter plate, eachportion may be of a different amount.

Example 16 Use of Kits

Kits may be used to perform the methods of the invention to provide adiagnosis or risk prediction for a subject having, or risk ofdeveloping, diabetes by enabling quantitation of the metabolites in ametabolite profile. For example, kits of the invention may be used todetermine if a subject has high risk of developing diabetes. Inaddition, kits of the invention may be used to determine if a subjecthas diabetes. In addition, kits of the invention may be used todetermine if a subject having diabetes is responding to a treatment fordiabetes.

A biological sample obtained from a subject having, or of high risk ofdeveloping, diabetes can be assessed using the kits of the invention.The sample may be a fluid sample (e.g., plasma or serum). In some usesof the kits, the metabolite profile in a subject sample may be assessedwithout processing of the sample. In other uses of the kits, themetabolite profile in a subject sample may require processing of thesample before being assessed.

A physician may take a sample from a subject and send the sample to aclinical laboratory for testing using the kits of the invention.Alternatively, the physician may be located at a clinical or medicalfacility that can perform testing using the kits of the invention.

The kits may be used to run a variety of tests to measure the amount ofone or more metabolites in a subject sample. For example, the kits maybe used to run a spectral analysis of a subject sample. Some kits areconfigured for spectral analyses such as gas chromatography and/orliquid chromatography. For example, a kit may be configured for LC-TQMSanalysis of the metabolites of interest in a subject sample.Alternatively, kits may be configured so that analytical tests specificfor different types of metabolites can be conducted (e.g., enzymatic,chemical, colorimetric, fluorometric, etc.) to measure the amount of themetabolites of interest in a subject's sample. In some uses, theinternal standards included in the kit are used as positive controls forthe analytical test performed to measure the amount of the metabolitesof interest in a subject sample. In some uses, the internal standardsincluded in the kit are used to help calibrate and/or measure the amountof the metabolites of interest in a subject sample. Depending on thetype of analytical tests to be conducted to measure the metabolites ofinterest in a subject sample, different components used to conduct theanalytical tests can be assembled into the kit with the one or moreinternal standards and the container.

The data obtained from the analytical tests performed using the kits isthe amount of each of one or more metabolites of interest (i.e.,metabolite markers) in a subject sample. This data can be compared toreference biomarker levels in healthy subjects.

After the data from the analytical tests performed using the kit areobtained (i.e., metabolite profile for the subject sample (i.e., amountof each metabolite of interest)), the data can be inputted into asoftware program located on a computer terminal in the laboratory togenerate a test result report, which can then be provided to thephysician or the individual. Once the physician receives the test resultreport from the clinical laboratory, the physician can evaluate thesubject's physical status. Based on the metabolite profile of thesubject's sample assessed, which, as noted above, the test result reportmay indicate to the physician that the subject either does not have ordoes have high risk of developing diabetes, that the subject isresponding to a particular treatment for diabetes (e.g., surgicaltreatment, medicine treatment, etc.). The physician can then, based onthe individual's status indicated by the test result report, providesuggestions or select an appropriate treatment for the subject, ifnecessary.

Example 17 Correlating Risk by Comparing a Biomarker Level to aComparator Level

The 62 overweight/obese subjects in Example 1 had normal metabolicmarkers at baseline and 50 of them developed diabetes (UO) while 12remained healthy (HO) according to their re-evaluation after ten years.

P/S ratio (C16:0/C18:0) was selected as a biomarker and ReceiverOperating Characteristic (ROC) analysis was further applied to evaluatethe performance of the P/S ratio in discriminating those 50 subjects whodeveloped diabetes after 10 years from the 12 remained healthyindividuals. The resultant ROC curve area of the P/S ratio was 0.76(0.60-0.91) (FIG. 6). According to the Youden's index (maximal value ofSensitivity+Specificity−1) from the ROC model, the optimal threshold ofthe P/S ratio was 1.15, which was used as a comparator value. Thus,overweight/obese subjects who have the P/S ratio greater than 1.15 mayhave high risk of diabetes in the future.

Example 18 Evaluating Diabetes Risk Using a Model for Scoring of aMulti-Biomarker Panel

The 62 overweight/obese subjects in Example 1 had normal metabolicmarkers at baseline and 50 of them developed diabetes (UO) while 12remained healthy (HO) according to their re-evaluation after ten years.

A panel comprising P/S ratio (C16:0/C18:0) and TG was selected.

Using logistical regression method, selected markers were combined as apanel and modeled according to the formula β₁X₁+β₂X₂+ . . . +α, where Xdenoting the absolute concentration or standardized value for the jthbiomarker, β_(j) denoting the coefficient from the regression model, andα denoting a constant value.

The model developed had the formula: Score=1.79*TG+3.67*P/S−4.57

ROC analysis was further applied to evaluate the performance of thecalculated score in discriminating those 50 subjects who developeddiabetes after 10 years from the 12 remained healthy individuals. Theresultant ROC curve area of the combined score was 0.78 (0.62-0.94)(FIG. 7). According to the Youden's index (maximal value ofSensitivity+Specificity−1) from the ROC model, the optimal threshold ofthe score was 1.58. Thus, overweight/obese subjects who have a scoregreater than 1.58 are identified has having high risk of developingdiabetes.

A sample is obtained from the subject and the P/S level and TG level ismeasured. The measured levels are input into the model formula to obtaina score as an output. The subject is identified as high risk fordeveloping diabetes if the score is greater than 1.58.

Example 19 Evaluating Diabetes Risk Using a Model for Scoring of aMulti-Biomarker Panel

The 62 overweight/obese subjects in Example 1 had normal metabolicmarkers at baseline and 50 of them developed diabetes (UO) while 12remained healthy (HO) according to their re-evaluation after ten years.

A panel was selected comprising P/S ratio (C16:0/C18:0), O/S ratio(C18:1 n9/C18:0) and DGLA/AA ratio (C20:3 n6/C20:4 n6).

Using logistical regression method, selected markers were combined as apanel and modeled according to the formula β₁X₁+β₂X₂+ . . . +α, where Xdenoting the absolute concentration or standardized value for the jthbiomarker, β_(j) denoting the coefficient from the regression model, anda denoting a constant value.

The model developed had the formula: Score=0.50*C16:0/C18:0+5.28*C18:1n9/C18:0+11.87*C20:3 n6/C20:4 n6−4.37.

ROC analysis was further applied to evaluate the performance of thecalculated score in discriminating those 50 subjects who developeddiabetes after 10 years from the 12 remained healthy individuals. Theresultant ROC curve area of the combined score was 0.82 (0.67-0.97)(FIG. 8). According to the Youden's index (maximal value ofSensitivity+Specificity−1) from the ROC model, the optimal threshold ofthe score was 1.19. Thus, overweight/obese subjects who have the panelscore greater than 1.19 are identified as having high risk of diabetesin the future.

A sample is obtained from the subject and the P/S level O/S level, andDGLA/AA level is measured. The measured levels are input into the modelformula to obtain a score as an output. The subject is identified ashigh risk for developing diabetes if the score is greater than 1.19.

Example 20 GLP1 Production Induced by Treatment with Bile Acids

Several bile acids were tested for their ability to increase productionof GLP-1. Without being bound by theory, the inventor believes thatcompounds which increase the production of GLP-1 provide ananti-diabetic effect.

CCL-241, a human small intestinal normal epithelial cell were treatedwith 100 μM of different bile acids and compared to that of wild type(WT) and DMSO treatment as controls. The expression of TGR5 and GLP1were detected by Immunofluorescence staining. The results are shown inFIG. 13. Generally, HCA, HDCA, GHDCA and THDCA exhibited the strongestGLP1-promotion effect. LCA, DCA and TCA also showed substantial GLP-1promotion effect.

Accordingly, this data demonstrates that subjects having a diabeticcondition can be treated with these bile acids or analogs thereof.

Further, subjects that naturally exhibit high levels of these bile acidsmay be protected or partially protected from developing diabetes.Accordingly, these bile acid may provide useful biomarkers forevaluating diabetes risk.

INCORPORATION BY REFERENCE

The citations (e.g. to patent and non-patent literature) provided hereinare hereby incorporated by reference for the cited subject matter.

What is claimed is:
 1. A method of determining the risk of a subjectdeveloping diabetes comprising: a. obtaining a biological sample fromthe subject; b. measuring the level of each biomarker of a panel in thesample, wherein the panel comprises at least one biomarker listed inFIG. 11, wherein said at least one biomarker comprises C16:0/C18:0ratio; c. correlating the measured level with risk of developingdiabetes; and d. determining the risk of the subject developing diabetesbased on the correlation; and e. optionally, administering a treatmentto the subject determined to be likely to develop diabetes.
 2. Themethod of claim 1, wherein the step of correlating comprises calculatinga score from the measured level of the at least one biomarker.
 3. Themethod of claim 2, wherein the score is output from a model wherein themodel is executed based on an input of the measured level of eachbiomarker of the panel.
 4. The method of claim 3, wherein the step ofdetermining comprises comparing the calculated score to a thresholdscore, wherein the subject is determined as likely to develop diabetesif the calculated score exceeds the threshold score.
 5. The method ofclaim 1, wherein the step of correlating comprises comparing themeasured level of each of the at least one biomarker to a respectivecomparator level.
 6. The method of claim 1, wherein the panel comprisesa panel selected from panels A, D, E, G, H, K, L, N, O, P, W, X, Y, Z,AC, AD, AF, AG, AJ, AK, AM, AN, AO, AV, AW, AX, BE, BF, BK, BN, BO, andBS listed in FIG. 12A-12C.
 7. The method of claim 1, wherein the atleast one biomarker of FIG. 11 comprises one or more of: a.Glycohyodeoxycholic acid (‘GHDCA’) or % GHDCA of total bile acids; b.Glycohyocholic acid (‘GHCA’) or % GHCA of total bile acids; c.Hyodeoxycholic acid (‘HDCA’) or % HDCA of total bile acids; d. Hyocholicacid (‘HCA’) or % HCA of total bile acids; e. Taurohyodeoxycholic acid(‘THDCA’) or % THDCA of total bile acids; f. C18:1 n9/C18:0 ratio; g.C20:3 n6/C20:4; h. Isoleucine, Leucine, Tyrosine, Valine, andPhenylalanine; and i. Total triglycerides (‘TG’).
 8. The method of claim1, wherein the at least one biomarker of FIG. 11 comprises; a. one ormore or all of the BCAAs of FIG. 11; b. one or both of phenylalanine andtyrosine; c. one or more or all of the free fatty acids of FIG. 11; d.one or more or all of the bile acids of FIG. 11; e. TG; or f. acombination of any two or three of a)-e).
 9. The method of claim 1,wherein the at least one biomarker of FIG. 11 comprises: a. GHDCA or %GHDCA of total bile acids, THDCA or % THDCA, HDCA or % HDCA of totalbile acids, HCA or % HCA of total bile acids, GHCA or % GHCA, and THCAor % THCA; b. Palmitic acid (C16:0), Stearic acid (C18:0), Oleic acid(C18:1 n9), dihomo-γ-linolenic acid (C20:3 n6), and Arachidonic acid(C20:4 n6); c. Isoleucine, Leucine, Tyrosine, Valine, and Phenylalanine;d. TG; e. a combination of a) and b), a) and c), b) and c), or each ofa), b), and c); f. a combination of (a) and (b), (a) and (c), (a) and(d), (b) and (c), (b) and (d), or (c) and (d); g. a combination (a),(b), and (c); h. a combination (a), (b), and (d); i. a combination (a),(c), and (d); or j. a combination (b), (c), and (d).
 10. The method ofclaim 1, wherein the at least one biomarker of FIG. 11 comprises: a. oneor more of GHDCA or % GHDCA of total bile acids, THDCA or % THDCA, HDCAor % HDCA of total bile acids, HCA or % HCA of total bile acids, GHCA or% GHCA, and THCA or % THCA b. one or more of Oleic acid (C18:1 n9),dihomo-γ-linolenic acid (C20:3 n6), and Arachidonic acid (C20:4 n6); c.one or more of Isoleucine, Leucine, Tyrosine, Valine, and Phenylalanine;d. TG; e. a combination of a) and b), a) and c), b) and c), or each ofa), b), a f. a combination of (a) and (b), (a) and (c), (a) and (d), (b)and (c), (b) and (d), or (c) and (d); g. a combination (a), (b), and(c); h. a combination (a), (b), and (d); i. a combination (a), (c), and(d); or j. a combination (b), (c), and (d).
 11. The method of claim 1,wherein the subject is any of: a. metabolically healthy; b. overweight;c. obese; d. not overweight; e. not obese; f. a) and b), or a) and c),and g. a) and d), or a) and e).
 12. A method of monitoring diabetestreatment of a subject comprising: a. obtaining a first biologicalsample from the subject and a second biological sample from the subject,wherein the first biological sample is obtained from the subject priorto the subject receiving a diabetes treatment and wherein the secondbiological sample is obtained from the subject following the subjectreceiving the diabetes treatment; b. measuring the level of at least onebiomarker of FIG. 11 in the first sample and the second sample, whereinsaid at least one biomarker comprises C16:0/C18:0 ratio; c. comparingthe measured level of the at least one biomarker in the first sample tothe measured level of the at least one biomarker in the second sample;d. determining the efficacy of the treatment based on a deviation in themeasured level of the at least one biomarker in the first samplerelative to the measured level of the at least one biomarker in thesecond sample; and e. changing or discontinuing the treatment if theefficacy is determined to be below a threshold.
 13. The method of claim4, further comprising a step of administering a diabetes treatment tothe subject when the calculated score exceeds the threshold score. 14.The method of claim 6, further comprising a step of administering, tothe subject determined to be likely to develop diabetes, a treatmentselected from the group consisting of metabolic surgery, carbohydraterestriction, dietary calorie restriction, a diet with less than 20 gramscarbohydrate per day, an exercise regimen, and an anti-diabetic agent.15. The method of claim 7, further comprising a step of administering,to the subject determined to be likely to develop diabetes, a treatmentselected from the group consisting of metabolic surgery, carbohydraterestriction, dietary calorie restriction, a diet with less than 20 gramscarbohydrate per day, an exercise regimen, and an anti-diabetic agent.16. The method of claim 8, further comprising a step of administering,to the subject determined to be likely to develop diabetes, a treatmentselected from the group consisting of metabolic surgery, carbohydraterestriction, dietary calorie restriction, a diet with less than 20 gramscarbohydrate per day, an exercise regimen, and an anti-diabetic agent.17. The method of claim 9, further comprising a step of administering,to the subject determined to be likely to develop diabetes, a treatmentselected from the group consisting of metabolic surgery, carbohydraterestriction, dietary calorie restriction, a diet with less than 20 gramscarbohydrate per day, an exercise regimen, and an anti-diabetic agent.18. The method of claim 10, further comprising a step of administering,to the subject determined to be likely to develop diabetes, a treatmentselected from the group consisting of metabolic surgery, carbohydraterestriction, dietary calorie restriction, a diet with less than 20 gramscarbohydrate per day, an exercise regimen, and an anti-diabetic agent.19. The method of claim 1, further comprising a step of administering,to the subject determined to be likely to develop diabetes, a treatmentselected from the group consisting of metabolic surgery, carbohydraterestriction, dietary calorie restriction, a diet with less than 20 gramscarbohydrate per day, an exercise regimen, and an anti-diabetic agent.